
@article{chaerle_multicolor_2007,
	title = {Multicolor fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus},
	volume = {164},
	issn = {0176-1617},
	url = {https://www.sciencedirect.com/science/article/pii/S0176161706000563},
	doi = {10.1016/j.jplph.2006.01.011},
	abstract = {The physiological status of plants can nowadays be promptly monitored with non-invasive methods. This opens the possibility to continuously follow-up plant performance and permits to detect stress-induced deviations presymptomatically. Upon stress, plants may synthesize specific compounds, depending on the causal agent. Such compounds may alter the absorption of the light impinging on plant leaves, hence the spectrum of reflected, re-emitted, and transmitted light changes. UV-excited fluorescence imaging specifically allows visualization of the accumulation of phenolic compounds, e.g. those associated with the hypersensitive response to pathogens. By using imaging at regular intervals (time-lapse series) of tobacco mosaic virus (TMV) infection in resistant tobacco we aimed at the description and quantification of the kinetics of blue–green fluorescence compared to the visual development of the disease. Presymptomatic responses to TMV infection were observed with a multicolor fluorescence and reflectance imaging setup. The onset of increases in blue–green and chlorophyll fluorescence were comparable in timing, although further symptom development was strikingly different. Compounds known to accumulate during the hypersensitive response and displaying blue–green fluorescence revealed different dynamics of fluorescence evolution in time. The multichannel imaging system permitted to discern the key components salicylic acid and scopoletin. In contrast, for the compatible interaction between TMV and non-resistant tobacco, no presymptomatic responses were detected on inoculated leaves. This work proves the potential of multispectral imaging to unveil stress-associated signatures, and the power of blue–green fluorescence imaging to monitor accumulation of secondary compounds.},
	language = {en},
	number = {3},
	urldate = {2021-04-28},
	journal = {J. Plant Physiol.},
	author = {Chaerle, Laury and Lenk, Sándor and Hagenbeek, Dik and Buschmann, Claus and Van Der Straeten, Dominique},
	month = mar,
	year = {2007},
	note = {Number: 3},
	keywords = {Blue–green fluorescence, Chlorophyll fluorescence, Plant stress, Salicylic acid, Scopoletin},
	pages = {253--262},
}

@article{rolfe_chlorophyll_2010,
	title = {Chlorophyll fluorescence imaging of plant–pathogen interactions},
	volume = {247},
	issn = {0033-183X, 1615-6102},
	url = {http://link.springer.com/10.1007/s00709-010-0203-z},
	doi = {10.1007/s00709-010-0203-z},
	abstract = {Chlorophyll fluorescence imaging provides a noninvasive, non-destructive method with which to measure heterogenous changes in photosynthetic metabolism in plants infected by pathogens. The availability of commercial imaging fluorimeters has helped make this technique available to the wider scientific community, but considerable care is needed, both in experimental design and in the interpretation of results, to make the most of this powerful analytical tool. The origins of changes in chlorophyll fluorescence yield are discussed and the use of conventional and novel combinatorial imaging approaches explored, together with complementary techniques such as thermal imaging. This review examines the use of chlorophyll fluorescence imaging as a method for the early detection of viral, bacterial and fungal infection, before symptoms are visible by eye, and also as a means with which to probe underlying pathogen-induced changes in host physiology in both compatible and incompatible interactions. The use of chlorophyll fluorescence imaging to study host physiology is greatly enhanced when the atmosphere around the leaf is manipulated and simultaneous measurements of gas exchange made: The cost to the host plant of different resistance mechanisms can be calculated, the fate of the products of photosynthetic electron transport determined and localised alterations in the source–sink status of host tissue visualised.},
	language = {en},
	number = {3-4},
	urldate = {2021-04-28},
	journal = {Protoplasma},
	author = {Rolfe, Stephen Alexander and Scholes, Julie Diane},
	month = dec,
	year = {2010},
	note = {Number: 3-4},
	pages = {163--175},
}

@article{rennie_point--care_2017,
	title = {Point-of-care fluorescence imaging predicts the presence of pathogenic bacteria in wounds: a clinical study},
	volume = {26},
	issn = {0969-0700},
	shorttitle = {Point-of-care fluorescence imaging predicts the presence of pathogenic bacteria in wounds},
	url = {https://www.magonlinelibrary.com/doi/abs/10.12968/jowc.2017.26.8.452},
	doi = {10.12968/jowc.2017.26.8.452},
	abstract = {Objective:Bacteria in chronic wounds are invisible to the naked eye and can lead to delayed wound healing. Point-of-care bacterial fluorescence imaging illuminates a wound with 405nm light, triggering bacteria to produce red fluorescence and enabling real-time bacterial localisation. Prospective, single-blind clinical trials (clinicaltrials.gov \#NCT02682069, \#NCT03091361) were conducted to determine the positive predictive value (PPV) of this red fluorescence for detecting bacteria in chronic wounds.Method:Lower limb chronic wounds were imaged for bacterial fluorescence using the MolecuLight i:X imaging device. Regions positive for red fluorescence were discretely sampled using either biopsy or curettage to correlate red fluorescence signals to bacterial presence and analysed via gold standard quantitative polymerase chain reaction (qPCR) or via semi-quantitative culture analysis respectively.Results:A total of 60 lower limb chronic wounds were imaged. Quantitative PCR analysis of wound tissue biopsies obtained from regions of red fluorescence yielded a PPV of 100\%. Total bacterial load in these areas was ≥104 CFU/g. Semi-quantitative culture analysis of curettage scrapings from regions of red fluorescence yielded a PPV of 100\%, with predominately moderate or heavy bacterial growth. There were nine distinct bacterial species detected, all common pathogens in chronic wounds. Staphylococcus aureus was the most prevalent species.Conclusion:Bacterial fluorescence image-guided curettage or biopsy sampling positively predicts bacterial presence in wounds at potentially harmful levels, entirely eliminating the risk of false negative sampling. Fluorescence imaging of wounds offers clinicians real-time information on a wound's bacterial burden, insight which can influence treatment decisions at the point-of care.},
	number = {8},
	urldate = {2021-04-28},
	journal = {J Wound Care},
	author = {Rennie, M.y. and Lindvere-Teene, L. and Tapang, K. and Linden, R.},
	month = aug,
	year = {2017},
	note = {Number: 8
Publisher: Mark Allen Group},
	pages = {452--460},
}

@article{marschall_compressed_2020,
	title = {Compressed {FTIR} spectroscopy using low-rank matrix reconstruction},
	volume = {28},
	issn = {1094-4087},
	url = {https://www.osapublishing.org/abstract.cfm?URI=oe-28-26-38762},
	doi = {10.1364/OE.404959},
	abstract = {Fourier transform infrared (FTIR) spectroscopy is a powerful technique in analytical chemistry. Typically, spatially distributed spectra of the substance of interest are conducted simultaneously using FTIR spectrometers equipped with array detectors. Scanning-based methods such as near-field FTIR spectroscopy, on the other hand, are a promising alternative providing higher spatial resolution. However, serial recording severely limits their application due to the long acquisition times involved and the resulting stability issues. We demonstrate that it is possible to significantly reduce the measurement time of scanning methods by applying the mathematical technique of low-rank matrix reconstruction. Data from a previous pilot study of Leishmania strains are analyzed by randomly selecting 5\% of the interferometer samples. The results obtained for bioanalytical fingerprinting using the proposed approach are shown to be essentially the same as those obtained from the full set of data. This finding can significantly foster the practical applicability of high-resolution serial scanning techniques in analytical chemistry and is also expected to improve other applications of FTIR spectroscopy and spectromicroscopy.},
	language = {en},
	number = {26},
	urldate = {2021-04-28},
	journal = {Opt. Express},
	author = {Marschall, Manuel and Hornemann, Andrea and Wübbeler, Gerd and Hoehl, Arne and Rühl, Eckart and Kästner, Bernd and Elster, Clemens},
	month = dec,
	year = {2020},
	note = {Number: 26},
	pages = {38762},
}

@article{wadduwage_near-common-path_2017,
	title = {Near-common-path interferometer for imaging {Fourier}-transform spectroscopy in wide-field microscopy},
	volume = {4},
	copyright = {\&\#169; 2017 Optical Society of America},
	issn = {2334-2536},
	url = {https://www.osapublishing.org/optica/abstract.cfm?uri=optica-4-5-546},
	doi = {10.1364/OPTICA.4.000546},
	abstract = {Imaging Fourier-transform spectroscopy (IFTS) is a powerful method for biological hyperspectral analysis based on various imaging modalities, such as fluorescence or Raman. Since the measurements are taken in the Fourier space of the spectrum, it can also take advantage of compressed sensing strategies. IFTS has been readily implemented in high-throughput, high-content microscope systems based on wide-field imaging modalities. However, there are limitations in existing wide-field IFTS designs. Non-common-path approaches are less phase-stable. Alternatively, designs based on the common-path Sagnac interferometer are stable, but incompatible with high-throughput imaging. They require exhaustive sequential scanning over large interferometric path delays, making compressive strategic data acquisition impossible. In this paper, we present a novel phase-stable, near-common-path interferometer enabling high-throughput hyperspectral imaging based on strategic data acquisition. Our results suggest that this approach can improve throughput over those of many other wide-field spectral techniques by more than an order of magnitude without compromising phase stability.},
	language = {EN},
	number = {5},
	urldate = {2021-04-28},
	journal = {Optica},
	author = {Wadduwage, Dushan N. and Singh, Vijay Raj and Choi, Heejin and Yaqoob, Zahid and Heemskerk, Hans and Matsudaira, Paul and So, Peter T. C.},
	month = may,
	year = {2017},
	note = {Number: 5
Publisher: Optical Society of America},
	pages = {546--556},
}

@article{studer_compressive_2012,
	title = {Compressive fluorescence microscopy for biological and hyperspectral imaging},
	volume = {109},
	issn = {0027-8424, 1091-6490},
	url = {https://www.pnas.org/content/109/26/E1679},
	doi = {10.1073/pnas.1119511109},
	abstract = {The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices—especially in optics—have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy.},
	language = {en},
	number = {26},
	urldate = {2021-04-28},
	journal = {Proc. Natl. Acad. Sci.},
	author = {Studer, Vincent and Bobin, Jérome and Chahid, Makhlad and Mousavi, Hamed Shams and Candes, Emmanuel and Dahan, Maxime},
	month = jun,
	year = {2012},
	pmid = {22689950},
	note = {Number: 26
Publisher: National Academy of Sciences
Section: PNAS Plus},
	keywords = {biological imaging, compressed sensing, computational imaging, sparse signals},
	pages = {E1679--E1687},
}

@article{candes_sparsity_2007,
	title = {Sparsity and incoherence in compressive sampling},
	volume = {23},
	issn = {0266-5611},
	url = {https://doi.org/10.1088/0266-5611/23/3/008},
	doi = {10.1088/0266-5611/23/3/008},
	abstract = {We consider the problem of reconstructing a sparse signal from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an orthonormal matrix, we show that ℓ1 minimization recovers x0 exactly when the number of measurements exceeds where S is the number of nonzero components in x0 and μ is the largest entry in U properly normalized: . The smaller μ is, the fewer samples needed. The result holds for ‘most’ sparse signals x0 supported on a fixed (but arbitrary) set T. Given T, if the sign of x0 for each nonzero entry on T and the observed values of Ux0 are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about as many samples.},
	language = {en},
	number = {3},
	urldate = {2021-04-28},
	journal = {Inverse Probl.},
	author = {Candès, Emmanuel and Romberg, Justin},
	month = apr,
	year = {2007},
	note = {Number: 3
Publisher: IOP Publishing},
	pages = {969--985},
}

@article{simonyan_very_2015,
	title = {Very {Deep} {Convolutional} {Networks} for {Large}-{Scale} {Image} {Recognition}},
	url = {http://arxiv.org/abs/1409.1556},
	abstract = {In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.},
	urldate = {2021-04-28},
	journal = {cs.CV},
	author = {Simonyan, Karen and Zisserman, Andrew},
	month = apr,
	year = {2015},
	note = {arXiv: 1409.1556},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{leshno_multilayer_1993,
	title = {Multilayer feedforward networks with a nonpolynomial activation function can approximate any function},
	volume = {6},
	issn = {0893-6080},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608005801315},
	doi = {10.1016/S0893-6080(05)80131-5},
	abstract = {Several researchers characterized the activation function under which multilayer feedforward networks can act as universal approximators. We show that most of all the characterizations that were reported thus far in the literature are special cases of the following general result: A standard multilayer feedforward network with a locally bounded piecewise continuous activation function can approximate any continuous function to any degree of accuracy if and only if the network's activation function is not a polynomial. We also emphasize the important role of the threshold, asserting that without it the last theorem does not hold.},
	language = {en},
	number = {6},
	urldate = {2021-04-28},
	journal = {Neural Netw.},
	author = {Leshno, Moshe and Lin, Vladimir Ya. and Pinkus, Allan and Schocken, Shimon},
	month = jan,
	year = {1993},
	note = {Number: 6},
	keywords = {(μ) approximation, Activation functions, Multilayer feedforward networks, Role of threshold, Universal approximation capabilities},
	pages = {861--867},
}

@article{kingma_adam_2017,
	title = {Adam: {A} {Method} for {Stochastic} {Optimization}},
	shorttitle = {Adam},
	url = {http://arxiv.org/abs/1412.6980},
	abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
	urldate = {2021-04-28},
	journal = {ICLR},
	author = {Kingma, Diederik P. and Ba, Jimmy},
	month = jan,
	year = {2017},
	note = {arXiv: 1412.6980},
	keywords = {Computer Science - Machine Learning},
}

@book{mozer_advances_1997,
	title = {Advances in {Neural} {Information} {Processing} {Systems} 9: {Proceedings} of the 1996 {Conference}},
	isbn = {978-0-262-10065-6},
	shorttitle = {Advances in {Neural} {Information} {Processing} {Systems} 9},
	abstract = {The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30\% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.},
	language = {en},
	publisher = {MIT Press},
	author = {Mozer, Michael C. and Jordan, Michael I. and Petsche, Thomas},
	year = {1997},
	note = {Google-Books-ID: QpD7n95ozWUC},
	keywords = {Computers / Data Science / Neural Networks},
}

@article{kastner_compressed_2018,
	title = {Compressed sensing {FTIR} nano-spectroscopy and nano-imaging},
	volume = {26},
	issn = {1094-4087},
	url = {https://www.osapublishing.org/abstract.cfm?URI=oe-26-14-18115},
	doi = {10.1364/OE.26.018115},
	abstract = {Infrared scattering scanning near-field optical microscopy (IR s-SNOM) provides for spectroscopic imaging with nanometer spatial resolution, yet full spatio-spectral imaging is constrained by long measurement times. Here, we demonstrate the application of compressed sensing algorithms to achieve hyperspectral FTIR-based nano-imaging at an order of magnitude faster imaging speed to achieve the same spectral content compared to conventional approaches. At the example of the spectroscopy of a single vibrational resonance, we discuss the relationship of prior knowledge of sparseness of the employed Fourier base functions and sub-sampling. Compressed sensing nano-FTIR spectroscopy promises both rapid and sensitive chemical nano-imaging which is highly relevant in academic and industrial settings for fundamental and applied nano- and bio-materials research.},
	language = {en},
	number = {14},
	urldate = {2021-04-28},
	journal = {Opt. Express},
	author = {Kästner, Bernd and Schmähling, Franko and Hornemann, Andrea and Ulrich, Georg and Hoehl, Arne and Kruskopf, Mattias and Pierz, Klaus and Raschke, Markus B. and Wübbeler, Gerd and Elster, Clemens},
	month = jul,
	year = {2018},
	note = {Number: 14},
	pages = {18115},
}

@article{heinecke_refinement_2020,
	title = {Refinement and {Universal} {Approximation} via {Sparsely} {Connected} {ReLU} {Convolution} {Nets}},
	volume = {27},
	issn = {1558-2361},
	doi = {10.1109/LSP.2020.3005051},
	abstract = {We construct a highly regular and simple structured class of sparsely connected convolutional neural networks with rectifier activations that provide universal function approximation in a coarse-to-fine manner with increasing number of layers. The networks are localized in the sense that local changes in the function to be approximated only require local changes in the final layer of weights. At the core of the construction lies the fact that the characteristic function can be derived from a convolution of characteristic functions at the next coarser resolution via a rectifier passing. The latter refinement result holds for all higher order univariate B-splines.},
	journal = {IEEE Signal Process. Lett.},
	author = {Heinecke, Andreas and Ho, Jinn and Hwang, Wen-Liang},
	year = {2020},
	note = {Conference Name: IEEE Signal Processing Letters},
	keywords = {Biological neural networks, Convolution, Convolutional neural networks, Function approximation, Indexes, neural networks, Neurons, splines, Splines (mathematics)},
	pages = {1175--1179},
}

@article{dunbar_accelerated_2013,
	title = {Accelerated {2D}-{IR} {Using} {Compressed} {Sensing}},
	volume = {4},
	issn = {1948-7185},
	url = {https://pubs.acs.org/doi/10.1021/jz401281r},
	doi = {10.1021/jz401281r},
	abstract = {A fundamental aspect of Fourier transform (FT) spectroscopy is the inverse relationship between frequency resolution and the maximum scanned time delay. In situations where essential chemical information is contained in spectral peak amplitudes rather than in their detailed shapes, it is possible to dramatically reduce the experimental acquisition time of time domain methods such as two-dimensional infrared (2D-IR) spectroscopy. By introducing compressed sensing to the analysis and experimental design of 2D-IR spectroscopy, we show that waiting-time-dependent 2D peak amplitudes reproduce conventional FT acquisition and analysis but can be recorded in a fraction of the time. Peak amplitude data are often suﬃcient for measuring intramolecular vibrational redistribution, vibrational coherence, chemical exchange, population, and orientational relaxation, as well as spectral diﬀusion.},
	language = {en},
	number = {15},
	urldate = {2021-04-28},
	journal = {J. Phys. Chem. Lett.},
	author = {Dunbar, Josef A. and Osborne, Derek G. and Anna, Jessica M. and Kubarych, Kevin J.},
	month = aug,
	year = {2013},
	note = {Number: 15},
	pages = {2489--2492},
}

@article{lee_deep_2017,
	title = {Deep {Learning} in {Medical} {Imaging}: {General} {Overview}},
	volume = {18},
	issn = {1229-6929},
	shorttitle = {Deep {Learning} in {Medical} {Imaging}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/},
	doi = {10.3348/kjr.2017.18.4.570},
	abstract = {The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.},
	number = {4},
	urldate = {2021-04-28},
	journal = {Korean J Radiol},
	author = {Lee, June-Goo and Jun, Sanghoon and Cho, Young-Won and Lee, Hyunna and Kim, Guk Bae and Seo, Joon Beom and Kim, Namkug},
	year = {2017},
	pmid = {28670152},
	pmcid = {PMC5447633},
	note = {Number: 4},
	pages = {570--584},
}

@article{iizuka_deep-learning-based_2019,
	title = {Deep-learning-based imaging-classification identified cingulate island sign in dementia with {Lewy} bodies},
	volume = {9},
	copyright = {2019 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-019-45415-5},
	doi = {10.1038/s41598-019-45415-5},
	abstract = {The differentiation of dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) using brain perfusion single photon emission tomography is important but is challenging because these conditions exhibit typical features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The current study aimed to examine the usefulness of deep-learning-based imaging classification for the diagnoses of DLB and AD. Furthermore, we investigated whether CIS was emphasized by a deep convolutional neural network (CNN) during differentiation. Brain perfusion single photon emission tomography images from 80 patients, each with DLB and AD, and 80 individuals with normal cognition (NL) were used for training and 20 each for final testing. The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the features that was emphasized by the trained CNN. The binary classifications between DLB and NL, DLB and AD, and AD and NL were 93.1\%, 89.3\%, and 92.4\% accurate, respectively. The CIS ratios closely correlated with the output scores before softmax for DLB–AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. The DLB/AD score was significantly associated with the three core features of DLB. Deep-learning-based imaging classification was useful for an objective and accurate differentiation of DLB from AD and for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning processes could be critical in deep learning to discover new imaging features.},
	language = {en},
	number = {1},
	urldate = {2021-04-28},
	journal = {Sci. Rep.},
	author = {Iizuka, Tomomichi and Fukasawa, Makoto and Kameyama, Masashi},
	month = jun,
	year = {2019},
	note = {Number: 1
Publisher: Nature Publishing Group},
	pages = {8944},
}

@article{hunter_advent_2019,
	title = {The advent of {AI} and deep learning in diagnostics and imaging},
	volume = {20},
	issn = {1469-221X},
	url = {https://www.embopress.org/doi/full/10.15252/embr.201948559},
	doi = {10.15252/embr.201948559},
	abstract = {Machine learning systems are increasingly applied in health care and the life sciences with great potential for cancer diagnostics and optical microscopy.},
	number = {7},
	urldate = {2021-04-28},
	journal = {EMBO Rep.},
	author = {Hunter, Philip},
	month = jul,
	year = {2019},
	note = {Number: 7
Publisher: John Wiley \& Sons, Ltd},
	pages = {e48559},
}

@article{rivenson_deep_2017,
	title = {Deep learning microscopy},
	volume = {4},
	copyright = {\&\#169; 2017 Optical Society of America},
	issn = {2334-2536},
	url = {https://www.osapublishing.org/optica/abstract.cfm?uri=optica-4-11-1437},
	doi = {10.1364/OPTICA.4.001437},
	abstract = {We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g.,\&\#x00A0;life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging.},
	language = {EN},
	number = {11},
	urldate = {2021-04-28},
	journal = {Optica},
	author = {Rivenson, Yair and Göröcs, Zoltán and Günaydin, Harun and Zhang, Yibo and Wang, Hongda and Ozcan, Aydogan},
	month = nov,
	year = {2017},
	note = {Number: 11
Publisher: Optical Society of America},
	pages = {1437--1443},
}

@article{esteva_dermatologist-level_2017,
	title = {Dermatologist-level classification of skin cancer with deep neural networks},
	volume = {542},
	copyright = {2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/nature21056},
	doi = {10.1038/nature21056},
	abstract = {An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.},
	language = {en},
	number = {7639},
	urldate = {2021-04-28},
	journal = {Nature},
	author = {Esteva, Andre and Kuprel, Brett and Novoa, Roberto A. and Ko, Justin and Swetter, Susan M. and Blau, Helen M. and Thrun, Sebastian},
	month = feb,
	year = {2017},
	note = {Number: 7639
Publisher: Nature Publishing Group},
	pages = {115--118},
}

@article{de_haan_resolution_2019,
	title = {Resolution enhancement in scanning electron microscopy using deep learning},
	volume = {9},
	copyright = {2019 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-019-48444-2},
	doi = {10.1038/s41598-019-48444-2},
	abstract = {We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.},
	language = {en},
	number = {1},
	urldate = {2021-04-28},
	journal = {Sci. Rep.},
	author = {de Haan, Kevin and Ballard, Zachary S. and Rivenson, Yair and Wu, Yichen and Ozcan, Aydogan},
	month = aug,
	year = {2019},
	note = {Number: 1
Publisher: Nature Publishing Group},
	pages = {12050},
}

@article{allikalt_fluorescent_2020,
	title = {Fluorescent ligands for dopamine {D} 2 /{D} 3 receptors},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-020-78827-9},
	doi = {10.1038/s41598-020-78827-9},
	abstract = {Fluorescent ligands are versatile tools for the study of G protein-coupled receptors. Depending on the fluorophore, they can be used for a range of different applications, including fluorescence microscopy and bioluminescence or fluorescence resonance energy transfer (BRET or FRET) assays. Starting from phenylpiperazines and indanylamines, privileged scaffolds for dopamine D2-like receptors, we developed dansyl-labeled fluorescent ligands that are well accommodated in the binding pockets of D2 and D3 receptors. These receptors are the target proteins for the therapy for several neurologic and psychiatric disorders, including Parkinson’s disease and schizophrenia. The dansyl-labeled ligands exhibit binding affinities up to 0.44 nM and 0.29 nM at D2R and D3R, respectively. When the dansyl label was exchanged for sterically more demanding xanthene or cyanine dyes, fluorescent ligands 10a-c retained excellent binding properties and, as expected from their indanylamine pharmacophore, acted as agonists at D2R. While the Cy3B-labeled ligand 10b was used to visualize D2R and D3R on the surface of living cells by total internal reflection microscopy, ligand 10a comprising a rhodamine label showed excellent properties in a NanoBRET binding assay at D3R.},
	language = {en},
	number = {1},
	urldate = {2021-04-28},
	journal = {Sci. Rep.},
	author = {Allikalt, Anni and Purkayastha, Nirupam and Flad, Khajidmaa and Schmidt, Maximilian F. and Tabor, Alina and Gmeiner, Peter and Hübner, Harald and Weikert, Dorothee},
	month = dec,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	pages = {21842},
}

@article{aida_deep_2020,
	title = {Deep {Learning} of {Cancer} {Stem} {Cell} {Morphology} {Using} {Conditional} {Generative} {Adversarial} {Networks}},
	volume = {10},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/2218-273X/10/6/931},
	doi = {10.3390/biom10060931},
	abstract = {Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.},
	language = {en},
	number = {6},
	urldate = {2021-04-28},
	journal = {Biomolecules},
	author = {Aida, Saori and Okugawa, Junpei and Fujisaka, Serena and Kasai, Tomonari and Kameda, Hiroyuki and Sugiyama, Tomoyasu},
	month = jun,
	year = {2020},
	note = {Number: 6
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Cancer stem cell, conditional generative adversarial network, green fluorescence protein, phase contrast, tumor},
	pages = {931},
}

@article{sung_spectroscopic_2018,
	title = {Spectroscopic {Microtomography} in the {Visible} {Wavelength} {Range}},
	volume = {10},
	url = {https://link.aps.org/doi/10.1103/PhysRevApplied.10.054041},
	doi = {10.1103/PhysRevApplied.10.054041},
	abstract = {We demonstrate spectroscopic microtomography in the visible wavelength range (450–700 nm) using digital holographic tomography, a wavelength-scanning source, and a variable-optical-path-length strategy. This multidimensional imaging system can directly measure the absorption-coefficient and refractive-index spectra of a three-dimensional specimen. We apply the system developed to characterize three types of polystyrene beads (undyed, blue dyed, and red dyed) as well as oxygenated and deoxygenated single red blood cells.},
	number = {5},
	urldate = {2021-04-28},
	journal = {Phys. Rev. Applied},
	author = {Sung, Yongjin},
	month = nov,
	year = {2018},
	note = {Number: 5
Publisher: American Physical Society},
	pages = {054041},
}

@article{jung_hyperspectral_2016,
	title = {Hyperspectral optical diffraction tomography},
	volume = {24},
	copyright = {\&\#169; 2016 Optical Society of America},
	issn = {1094-4087},
	url = {https://www.osapublishing.org/oe/abstract.cfm?uri=oe-24-3-2006},
	doi = {10.1364/OE.24.002006},
	abstract = {Here, we present a novel microscopic technique for measuring wavelength-dependent three-dimensional (3-D) distributions of the refractive indices (RIs) of microscopic samples in the visible wavelengths. Employing 3-D quantitative phase microscopy techniques with a wavelength-swept source, 3-D RI tomograms were obtained in the range of 450 \&\#x2013; 700 nm with a spectral resolution of a few nanometers. The capability of the technique was demonstrated by measuring the hyperspectral 3-D RI tomograms of polystyrene beads, human red blood cells, and hepatocytes. The results demonstrate the potential for label-free molecular specific 3-D tomography of biological samples.},
	language = {EN},
	number = {3},
	urldate = {2021-04-28},
	journal = {Opt. Express},
	author = {Jung, JaeHwang and Kim, Kyoohyun and Yoon, Jonghee and Park, YongKeun},
	month = feb,
	year = {2016},
	note = {Number: 3
Publisher: Optical Society of America},
	pages = {2006--2012},
}

@article{morgner_spectroscopic_2000,
	title = {Spectroscopic optical coherence tomography},
	volume = {25},
	copyright = {\&\#169; 2000 Optical Society of America},
	issn = {1539-4794},
	url = {https://www.osapublishing.org/ol/abstract.cfm?uri=ol-25-2-111},
	doi = {10.1364/OL.25.000111},
	abstract = {Spectroscopic optical coherence tomography (OCT), an extension of conventional OCT, is demonstrated for performing cross-sectional tomographic and spectroscopic imaging. Information on the spectral content of backscattered light is obtained by detection and processing of the interferometric OCT signal. This method allows the spectrum of backscattered light to be measured over the entire available optical bandwidth simultaneously in a single measurement. Specific spectral features can be extracted by use of digital signal processing without changing the measurement apparatus. An ultrabroadband femtosecond Ti:Al2O3 laser was used to achieve spectroscopic imaging over the wavelength range from 650 to 1000 nm in a simple model as well as in vivo in the Xenopus laevis (African frog) tadpole. Multidimensional spectroscopic data are displayed by use of a novel hue-saturation false-color mapping.},
	language = {EN},
	number = {2},
	urldate = {2021-04-28},
	journal = {Opt. Lett., OL},
	author = {Morgner, U. and Drexler, W. and Kärtner, F. X. and Li, X. D. and Pitris, C. and Ippen, E. P. and Fujimoto, J. G.},
	month = jan,
	year = {2000},
	note = {Number: 2
Publisher: Optical Society of America},
	keywords = {Functional imaging, Imaging techniques, In vivo imaging, Interferometry, Optical coherence tomography, Point spread function},
	pages = {111--113},
}

@article{oldenburg_spectroscopic_2007,
	title = {Spectroscopic {Optical} {Coherence} {Tomography} and {Microscopy}},
	volume = {13},
	issn = {1558-4542},
	doi = {10.1109/JSTQE.2007.910292},
	abstract = {Imaging biological tissues using optical coherence tomography is enhanced with spectroscopic analysis, providing new metrics for functional imaging. Recent advances in spectroscopic optical coherence tomography (SOCT) include techniques for the discrimination of endogenous tissue types and for the detection of exogenous contrast agents. In this paper, we review these techniques and their associated signal processing algorithms, while highlighting their potential for biomedical applications. We unify the theoretical framework for time- and frequency-domain SOCT and introduce a noise correction method. Differences between spectroscopic Mie scatterers are demonstrated with SOCT, and spectroscopic imaging of macrophage and fibroblast cells in a 3-D scaffold is shown.},
	number = {6},
	journal = {IEEE J Sel Top Quantum Electron},
	author = {Oldenburg, Amy L. and Xu, Chenyang and Boppart, Stephen A.},
	month = nov,
	year = {2007},
	note = {Number: 6
Conference Name: IEEE Journal of Selected Topics in Quantum Electronics},
	keywords = {Biological tissues, Biomedical imaging, Biomedical optical imaging, Biophotonics, functional imaging, Image analysis, molecular imaging, optical coherence tomography, Optical imaging, Optical microscopy, Optical scattering, Optical signal processing, spectroscopy, Spectroscopy, tissue, Tomography},
	pages = {1629--1640},
}

@article{sinclair_hyperspectral_2006,
	title = {Hyperspectral confocal microscope},
	volume = {45},
	issn = {2155-3165},
	url = {https://www.osapublishing.org/ao/abstract.cfm?uri=ao-45-24-6283},
	doi = {10.1364/AO.45.006283},
	abstract = {We have developed a new, high performance, hyperspectral microscope for biological and other applications. For each voxel within a three-dimensional specimen, the microscope simultaneously records the emission spectrum from 500 nm to 800 nm, with better than 3 nm spectral resolution. The microscope features a fully confocal design to ensure high spatial resolution and high quality optical sectioning. Optical throughput and detection efficiency are maximized through the use of a custom prism spectrometer and a backside thinned electron multiplying charge coupled device (EMCCD) array. A custom readout mode and synchronization scheme enable 512-point spectra to be recorded at a rate of 8300 spectra per second. In addition, the EMCCD readout mode eliminates curvature and keystone artifacts that often plague spectral imaging systems. The architecture of the new microscope is described in detail, and hyperspectral images from several specimens are presented.},
	language = {EN},
	number = {24},
	urldate = {2021-04-28},
	journal = {Appl. Opt.},
	author = {Sinclair, Michael B. and Haaland, David M. and Timlin, Jerilyn A. and Jones, Howland D. T.},
	month = aug,
	year = {2006},
	note = {Number: 24
Publisher: Optical Society of America},
	keywords = {Confocal microscopy, Fluorescence microscopy, Hyperspectral imaging, Imaging systems, Optical design, Spectral imaging},
	pages = {6283--6291},
}

@article{jahr_hyperspectral_2015,
	title = {Hyperspectral light sheet microscopy},
	volume = {6},
	copyright = {2015 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/ncomms8990},
	doi = {10.1038/ncomms8990},
	abstract = {To study the development and interactions of cells and tissues, multiple fluorescent markers need to be imaged efficiently in a single living organism. Instead of acquiring individual colours sequentially with filters, we created a platform based on line-scanning light sheet microscopy to record the entire spectrum for each pixel in a three-dimensional volume. We evaluated data sets with varying spectral sampling and determined the optimal channel width to be around 5 nm. With the help of these data sets, we show that our setup outperforms filter-based approaches with regard to image quality and discrimination of fluorophores. By spectral unmixing we resolved overlapping fluorophores with up to nanometre resolution and removed autofluorescence in zebrafish and fruit fly embryos.},
	language = {en},
	number = {1},
	urldate = {2021-04-28},
	journal = {Nat. Commun.},
	author = {Jahr, Wiebke and Schmid, Benjamin and Schmied, Christopher and Fahrbach, Florian O. and Huisken, Jan},
	month = sep,
	year = {2015},
	note = {Number: 1
Publisher: Nature Publishing Group},
	pages = {7990},
}

@article{wu_snapshot_2016,
	title = {Snapshot {Hyperspectral} {Volumetric} {Microscopy}},
	volume = {6},
	copyright = {2016 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/srep24624},
	doi = {10.1038/srep24624},
	abstract = {The comprehensive analysis of biological specimens brings about the demand for capturing the spatial, temporal and spectral dimensions of visual information together. However, such high-dimensional video acquisition faces major challenges in developing large data throughput and effective multiplexing techniques. Here, we report the snapshot hyperspectral volumetric microscopy that computationally reconstructs hyperspectral profiles for high-resolution volumes of {\textasciitilde}1000 μm × 1000 μm × 500 μm at video rate by a novel four-dimensional (4D) deconvolution algorithm. We validated the proposed approach with both numerical simulations for quantitative evaluation and various real experimental results on the prototype system. Different applications such as biological component analysis in bright field and spectral unmixing of multiple fluorescence are demonstrated. The experiments on moving fluorescent beads and GFP labelled drosophila larvae indicate the great potential of our method for observing multiple fluorescent markers in dynamic specimens.},
	language = {en},
	number = {1},
	urldate = {2021-04-28},
	journal = {Sci. Rep.},
	author = {Wu, Jiamin and Xiong, Bo and Lin, Xing and He, Jijun and Suo, Jinli and Dai, Qionghai},
	month = apr,
	year = {2016},
	note = {Number: 1
Publisher: Nature Publishing Group},
	pages = {24624},
}

@article{yao_spectral-depth_2019,
	title = {Spectral-depth imaging with deep learning based reconstruction},
	volume = {27},
	issn = {1094-4087},
	url = {https://www.osapublishing.org/abstract.cfm?URI=oe-27-26-38312},
	doi = {10.1364/OE.27.038312},
	abstract = {We develop a compact imaging system to enable simultaneous acquisition of the spectral and depth information in real time. Our system consists of a spectral camera with low spatial resolution and an RGB camera with high spatial resolution, which captures two measurements from two diﬀerent views of the same scene at the same time. Relying on an elaborate computational reconstruction algorithm with deep learning, our system can eventually obtain a spectral cube with a spatial resolution of 1920 × 1080 and a total of 16 spectral bands in the visible light section, as well as the corresponding depth map with the same spatial resolution. Quantitative and qualitative results on benchmark datasets and real-world scenes show that our reconstruction results are accurate and reliable. To the best of our knowledge, this is the ﬁrst attempt to capture 5D information (3D space + 1D spectrum + 1D time) with a miniaturized apparatus and without active illumination.},
	language = {en},
	number = {26},
	urldate = {2021-04-28},
	journal = {Opt. Express},
	author = {Yao, Mingde and Xiong, Zhiwei and Wang, Lizhi and Liu, Dong and Chen, Xuejin},
	month = dec,
	year = {2019},
	note = {Number: 26},
	pages = {38312},
}

@article{obst_soft_2009,
	title = {Soft {X}-ray spectro-tomography study of cyanobacterial biomineral nucleation},
	volume = {7},
	copyright = {© 2009 Blackwell Publishing Ltd},
	issn = {1472-4669},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1472-4669.2009.00221.x},
	doi = {https://doi.org/10.1111/j.1472-4669.2009.00221.x},
	abstract = {Quantitative three-dimensional (3D) chemical mapping using angle-scan spectro-tomography in a scanning transmission (soft) X-ray microscope (STXM) has been used for the first time to characterize the early stages of CaCO3 biomineral nucleation on the surface of planktonic freshwater cyanobacterial cells of the strain Synechococcus leopoliensis PCC 7942. The apparatus for STXM angle-scan tomography is described. Aspects of sample preparation, sample mounting and data acquisition and quantitative analysis and interpretation are discussed in detail. Angle-scan tomography and chemically selective 3D imaging at multiple photon energies has been combined with a complete 2D spectromicroscopic characterization of the biochemical and mineralogical composition. This has provided detailed insights into the mechanisms of mineral nucleation, leading to development of a detailed model of CaCO3 nucleation by the cyanobacterial strain S. leopoliensis PCC 7942. It shows that Ca is absorbed by the extracellular polymeric substances (EPS) of the cyanobacteria and that CaCO3 with aragonite-like short-range order is precipitated rather homogeneously within the EPS. The precipitation of the thermodynamically more stable calcite polymorph then starts at Ca-rich hot spots within the EPS and close to the cyanobacteria.},
	language = {en},
	number = {5},
	urldate = {2021-04-28},
	journal = {Geobiology},
	author = {Obst, M. and Wang, J. and Hitchcock, A. P.},
	year = {2009},
	note = {Number: 5
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1472-4669.2009.00221.x},
	pages = {577--591},
}

@article{li_hyperband_2018,
	title = {Hyperband: {A} {Novel} {Bandit}-{Based} {Approach} to {Hyperparameter} {Optimization}},
	abstract = {Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select conﬁgurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration nonstochastic inﬁnite-armed bandit problem where a predeﬁned resource like iterations, data samples, or features is allocated to randomly sampled conﬁgurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.},
	language = {en},
	journal = {J Mach Learn Res},
	author = {Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
	year = {2018},
	pages = {52},
}

@article{leavesley_hyperspectral_2012,
	title = {Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue},
	volume = {5},
	number = {1},
	journal = {J. Biophotonics},
	author = {Leavesley, Silas J. and Annamdevula, Naga and Boni, John and Stocker, Samantha and Grant, Kristin and Troyanovsky, Boris and Rich, Thomas C. and Alvarez, Diego F.},
	year = {2012},
	note = {Number: 1
Publisher: Wiley Online Library},
	pages = {67--84},
}

@article{zimmermann_spectral_2003,
	title = {Spectral imaging and its applications in live cell microscopy},
	volume = {546},
	number = {1},
	journal = {FEBS Lett.},
	author = {Zimmermann, Timo and Rietdorf, Jens and Pepperkok, Rainer},
	year = {2003},
	note = {Number: 1
Publisher: Wiley Online Library},
	pages = {87--92},
}

@article{lohumi_lctf-based_2021,
	title = {{LCTF}-based multispectral fluorescence imaging: {System} development and potential for real-time foreign object detection in fresh-cut vegetable processing},
	volume = {180},
	issn = {0168-1699},
	shorttitle = {{LCTF}-based multispectral fluorescence imaging},
	url = {https://www.sciencedirect.com/science/article/pii/S0168169920331173},
	doi = {10.1016/j.compag.2020.105912},
	abstract = {Ensuring the supply of safe and contaminant-free fresh-cut vegetables is of importance to consumers and suppliers worldwide. The present research aimed to develop an online inspection system, making use of fluorescence and color imaging for detection of potential foreign materials (FMs) unintentionally added to fresh-cut vegetables. A multispectral fluorescence imager incorporating a liquid crystal tunable filter (LCTF) was developed to sequentially acquire selected band images of fresh-cut vegetables. Custom-built software was developed to synchronize the LCTF with the camera exposure to collect the fluorescence images of selected spectral bands and to collect the digital color images of the same samples using a color camera. Image processing algorithms were developed for both fluorescence and color images to detect a wide range of potential FMs in two different fresh-cut vegetables, namely, cabbage and green onion. The obtained results showed that the combined use of the multispectral fluorescence technique and color imaging can detect most FMs with accuracy {\textgreater} 95\%; however, non-fluorescent tiny dark FMs could not be detected. The potential of the developed online detection system was further tested in an industrial environment by installation in a fresh-cut processing unit. All hardware parts were enclosed in a black box and the software program was modified to programmatically control conveyor belt movement and facilitate real-time image processing for FM visualization. FMs mixed with fresh-cut vegetables were manually fed onto the conveyor belt; fluorescence and color images of fresh-cut vegetables were acquired simultaneously while moving through the conveyor belt. The developed online system performed almost identically in the industrial environment, and has the potential to scan an area as large as 24 × 24 cm2 in approximately 1.5 s. Thus, its application in commercial setting may be feasible.},
	language = {en},
	urldate = {2021-07-19},
	journal = {Comput Electron Agric},
	author = {Lohumi, Santosh and Cho, Byoung-Kwan and Hong, Sangdeok},
	month = jan,
	year = {2021},
	keywords = {Fluorescence multispectral imaging, Foreign materials, Fresh-cut vegetables, Liquid crystal tunable filter, On-line detection},
	pages = {105912},
}

@article{tsurui_seven-color_2000,
	title = {Seven-color {Fluorescence} {Imaging} of {Tissue} {Samples} {Based} on {Fourier} {Spectroscopy} and {Singular} {Value} {Decomposition}},
	volume = {48},
	issn = {0022-1554},
	url = {https://doi.org/10.1177/002215540004800509},
	doi = {10.1177/002215540004800509},
	abstract = {SUMMARY Seven-color analyses of immunofluorescence-stained tissue samples were accomplished using Fourier spectroscopy-based hyperspectral imaging and singular value decomposition. This system consists of a combination of seven fluorescent dyes, three filter-sets, an epifluorescence microscope, a spectral imaging system, a computer for data acquisition, and data analysis software. The spectra of all pixels in a multicolor image were taken simultaneously using a Sagnac type interferometer. The spectra were deconvolved to estimate the contribution of each component dye, and individual dye images were constructed based on the intensities of assigned signals. To obtain mixed spectra, three filter sets, i.e., Bl, Gr, and Rd for Alexa488 and Alexa532, for Alexa546, Alexa568, and Alexa594, and for Cy5 and Cy5.5, respectively, were used for simultaneous excitation of two or three dyes. These fluorophores have considerable spectral overlap which precludes their separation by conventional analysis. We resolved their relative contributions to the fluorescent signal by a method involving linear unmixing based on singular value decomposition of the matrices consisting of dye spectra. Analyses of mouse thymic tissues stained with seven different fluorescent dyes provided clear independent images, and any combination of two or three individual dye images could be used for constructing multicolor images.},
	number = {5},
	urldate = {2021-07-19},
	journal = {J Histochem Cytochem.},
	author = {Tsurui, Hiromichi and Nishimura, Hiroyuki and Hattori, Susumu and Hirose, Sachiko and Okumura, Ko and Shirai, Toshikazu},
	month = may,
	year = {2000},
	note = {Number: 5
Publisher: Journal of Histochemistry \& Cytochemistry},
	pages = {653--662},
}

@book{goodfellow_deep_2016,
	title = {Deep {Learning}},
	isbn = {978-0-262-33737-3},
	abstract = {An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.},
	language = {en},
	publisher = {MIT Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	month = nov,
	year = {2016},
	note = {Google-Books-ID: omivDQAAQBAJ},
	keywords = {Computers / Artificial Intelligence / General, Computers / Computer Science},
}

@article{krizhevsky_imagenet_2017,
	title = {{ImageNet} classification with deep convolutional neural networks},
	volume = {60},
	issn = {0001-0782, 1557-7317},
	url = {https://dl.acm.org/doi/10.1145/3065386},
	doi = {10.1145/3065386},
	abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5\% and 17.0\% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of ﬁve convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a ﬁnal 1000-way softmax. To make training faster, we used non-saturating neurons and a very efﬁcient GPU implementation of the convolution operation. To reduce overﬁtting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3\%, compared to 26.2\% achieved by the second-best entry.},
	language = {en},
	number = {6},
	urldate = {2021-07-20},
	journal = {Commun. ACM},
	author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
	month = may,
	year = {2017},
	note = {Number: 6},
	pages = {84--90},
}

@article{aggarwal_neural_2018,
	title = {Neural networks and deep learning},
	volume = {10},
	journal = {Springer},
	author = {Aggarwal, Charu C.},
	year = {2018},
	note = {Publisher: Springer},
	pages = {978--3},
}

@article{kremers_fluorescent_2011,
	title = {Fluorescent proteins at a glance},
	volume = {124},
	issn = {1477-9137, 0021-9533},
	url = {https://journals.biologists.com/jcs/article/124/15/2676/31805/Fluorescent-proteins-at-a-glance},
	doi = {10.1242/jcs.095059},
	language = {en},
	number = {15},
	urldate = {2021-07-20},
	journal = {J. Cell Sci.},
	author = {Kremers, Gert-Jan and Gilbert, Sarah G. and Cranfill, Paula J. and Davidson, Michael W. and Piston, David W.},
	month = aug,
	year = {2011},
	note = {Number: 15},
	pages = {2676--2676},
}

@article{imataka_chromosome_2012,
	title = {Chromosome {Analysis} {Using} {Spectral} {Karyotyping} ({SKY})},
	volume = {62},
	issn = {1085-9195, 1559-0283},
	url = {http://link.springer.com/10.1007/s12013-011-9285-2},
	doi = {10.1007/s12013-011-9285-2},
	abstract = {Spectral karyotyping is a novel technique for chromosome analysis that has been developed based on the approach of the ﬂuorescence in situ hybridization technique. Spectral karyotyping makes it feasible to diagnose a variety of diseases, because of its technology in painting each of the 24 human chromosomes with different colors. In recent years, it has become possible to adopt the usage of spectral karyotyping for research in general clinical practice, and its usability has attracted particular attention in the diagnosis of different diseases. In this review, we will explain the principle of the spectral karyotyping, as well as its speciﬁcity and limitation in detecting the genetic defects within clinical application by presenting two case reports.},
	language = {en},
	number = {1},
	urldate = {2021-07-20},
	journal = {Cell Biochem. Biophys.},
	author = {Imataka, George and Arisaka, Osamu},
	month = jan,
	year = {2012},
	note = {Number: 1},
	pages = {13--17},
}

@book{brownlee_better_2018,
	title = {Better {Deep} {Learning}: {Train} {Faster}, {Reduce} {Overfitting}, and {Make} {Better} {Predictions}},
	shorttitle = {Better {Deep} {Learning}},
	abstract = {Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.},
	language = {en},
	publisher = {Machine Learning Mastery},
	author = {Brownlee, Jason},
	month = dec,
	year = {2018},
	keywords = {Computers / Programming / Algorithms},
}

@book{griffiths_fourier_2007,
	title = {Fourier {Transform} {Infrared} {Spectrometry}},
	isbn = {978-0-470-10629-7},
	abstract = {A bestselling classic reference, now expanded and updated to cover the latest instrumentation, methods, and applications   The Second Edition of Fourier Transform Infrared Spectrometry brings this core reference up to date on the uses of FT-IR spectrometers today. The book starts with an in-depth description of the theory and current instrumentation of FT-IR spectrometry, with full chapters devoted to signal-to-noise ratio and photometric accuracy. Many diverse types of sampling techniques and data processing routines, most of which can be performed on even the less expensive instruments, are then described. Extensively updated, the Second Edition: *   Discusses improvements in optical components *   Features a full chapter on FT Raman Spectrometry *   Contains new chapters that focus on different ways of measuring spectra by FT-IR spectrometry, including fourteen chapters on such techniques as microspectroscopy, internal and external reflection, and emission and photoacoustic spectrometry *   Includes a new chapter introducing the theory of vibrational spectrometry  *   Organizes material according to sampling techniques   Designed to help practitioners using FT-IR capitalize on the plethora of techniques for modern FT-IR spectrometry and plan their experimental procedures correctly, this is a practical, hands-on reference for chemists and analysts. It's also a great resource for students who need to understand the theory, instrumentation, and applications of FT-IR.},
	language = {en},
	publisher = {John Wiley \& Sons},
	author = {Griffiths, Peter R. and Haseth, James A. De},
	month = mar,
	year = {2007},
	note = {Google-Books-ID: C\_c0GVe8MX0C},
	keywords = {Science / Chemistry / General, Science / Spectroscopy \& Spectrum Analysis},
}

@inproceedings{tsurui_hyperspectral_1999,
	title = {Hyperspectral imaging of pathology samples},
	volume = {3605},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/3605/0000/Hyperspectral-imaging-of-pathology-samples/10.1117/12.347573.short},
	doi = {10.1117/12.347573},
	abstract = {The object of the experiment described in this paper was to demonstrate that cells stained with multiple fluorophores could be identified and quantified simultaneously. Hyperspectral imaging was used to classify spleen cells of a Balb/c mouse, with the anti-mouse CD4 antibody conjugated with Alexa 488, 532, 546 and 568. It was found that the system was able to identify the specific fluorophore present and map their location in the cells. The system also provided relative signal strength data. Spectral libraries were constructed with color-coded spectra that enabled automatic spectral identification in subsequent acquisitions.},
	urldate = {2021-08-10},
	booktitle = {Three-{Dimensional} and {Multidimensional} {Microscopy}: {Image} {Acquisition} and {Processing} {VI}},
	publisher = {SPIE},
	author = {Tsurui, Hiromichi and Lerner, Jeremy M. and Takahashi, Kuniaki and Hirose, Sachiko and Mitsui, K. and Okumura, Ko and Shirai, Toshikazu},
	month = may,
	year = {1999},
	pages = {273--281},
	file = {Snapshot:C\:\\Users\\juntune3\\Zotero\\storage\\9NVPS8W8\\12.347573.html:text/html},
}

@article{wachman_aotf_1997,
	title = {{AOTF} microscope for imaging with increased speed and spectral versatility},
	volume = {73},
	issn = {0006-3495},
	doi = {10.1016/S0006-3495(97)78154-2},
	abstract = {We have developed a new fluorescence microscope that addresses the spectral and speed limitations of current light microscopy instrumentation. In the present device, interference and neutral density filters normally used for fluorescence excitation and detection are replaced by acousto-optic tunable filters (AOTFs). Improvements are described, including the use of a dispersing prism in conjunction with the imaging AOTF and an oblique-illumination excitation scheme, which together enable the AOTF microscope to produce images comparable to those obtained with conventional fluorescence instruments. The superior speed and spectral versatility of the AOTF microscope are demonstrated by a ratio image pair acquired in 3.5 ms and a micro-spectral absorbance measurement of hemoglobin through a cranial window in a living mouse.},
	language = {eng},
	number = {3},
	journal = {Biophys J},
	author = {Wachman, E. S. and Niu, W. and Farkas, D. L.},
	month = sep,
	year = {1997},
	pmid = {9284289},
	pmcid = {PMC1181021},
	keywords = {3T3 Cells, Acoustics, Animals, Cerebral Arteries, Cerebral Cortex, Cerebral Veins, Equipment Design, Least-Squares Analysis, Mice, Microscopy, Fluorescence, Optics and Photonics, Sensitivity and Specificity, Spectrometry, Fluorescence, Spectrophotometry},
	pages = {1215--1222},
	file = {Full Text:C\:\\Users\\juntune3\\Zotero\\storage\\DT3PF63P\\Wachman et al. - 1997 - AOTF microscope for imaging with increased speed a.pdf:application/pdf},
}

@inproceedings{abdlaty_hyperspectral_2018,
	title = {Hyperspectral imaging: comparison of acousto-optic and liquid crystal tunable filters},
	volume = {10573},
	shorttitle = {Hyperspectral imaging},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10573/105732P/Hyperspectral-imaging--comparison-of-acousto-optic-and-liquid-crystal/10.1117/12.2282532.short},
	doi = {10.1117/12.2282532},
	abstract = {In this work, we report a performance comparison of an acousto-optic tunable filter (AOTF), and a liquid crystal tunable filter (LCTF) based on a novel dual-arm hyperspectral imaging (HSI) configuration. The main purpose of this work is to highlight the leverage points of each tunable filter, in order to facilitate filter choice in HSI design. Three main parameters are experimentally examined: spectral resolution, out-of-band suppression, and image quality in the sense of spatial resolution. The experimental results, using wideband illumination, laser lines, and a spatial test target (USAF-1951) emphasized the superiority of AOTF in spectral resolution, out-of –band suppression and random switching speed between wavelengths. The same experiments demonstrated LCTF to have better performance in terms of the spatial image resolution, both horizontal and vertical, and high definition quality. In conclusion, the efficient design of an HSI system is application-dependent. For medical applications, for instance, if the tissue of interest has undefined optical properties, or contains close spectral features, AOTF might be the better option. Otherwise, LCTF is more convenient and simpler to use, especially if the tissue chromophore’s spatial mapping is needed.},
	urldate = {2021-08-10},
	booktitle = {Medical {Imaging} 2018: {Physics} of {Medical} {Imaging}},
	publisher = {SPIE},
	author = {Abdlaty, Ramy and Sahli, Samir and Hayward, Joseph and Fang, Qiyin},
	month = mar,
	year = {2018},
	pages = {105732P},
	file = {Snapshot:C\:\\Users\\juntune3\\Zotero\\storage\\N85QQB49\\12.2282532.html:text/html},
}

@article{donoho_compressed_2006,
	title = {Compressed sensing},
	volume = {52},
	issn = {1557-9654},
	doi = {10.1109/TIT.2006.871582},
	abstract = {Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0{\textless}ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0{\textless}ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces},
	number = {4},
	journal = {IEEE Trans. Inf. Theory},
	author = {Donoho, D.L.},
	month = apr,
	year = {2006},
	note = {Conference Name: IEEE Transactions on Information Theory},
	keywords = {Adaptive sampling, almost-spherical sections of Banach spaces, Basis Pursuit, Compressed sensing, Data mining, Digital images, eigenvalues of random matrices, Gel'fand, Image coding, Image reconstruction, information-based complexity, integrated sensing and processing, minimum, optimal recovery, Pixel, Quotient-of-a-Subspace theorem, Signal processing, Size measurement, sparse solution of linear equations, Transform coding, Vectors},
	pages = {1289--1306},
	file = {IEEE Xplore Full Text PDF:C\:\\Users\\juntune3\\Zotero\\storage\\SKMJSSH6\\Donoho - 2006 - Compressed sensing.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\juntune3\\Zotero\\storage\\QB5GYYS4\\1614066.html:text/html},
}

@book{eldar_compressed_2012,
	title = {Compressed {Sensing}: {Theory} and {Applications}},
	isbn = {978-1-107-39439-1},
	shorttitle = {Compressed {Sensing}},
	abstract = {Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.},
	language = {en},
	publisher = {Cambridge University Press},
	author = {Eldar, Yonina C. and Kutyniok, Gitta},
	month = may,
	year = {2012},
	note = {Google-Books-ID: 9ccLAQAAQBAJ},
	keywords = {Technology \& Engineering / Electronics / General, Technology \& Engineering / Manufacturing, Technology \& Engineering / Materials Science / General, Technology \& Engineering / Mechanical, Technology \& Engineering / Signals \& Signal Processing, Technology \& Engineering / Technical \& Manufacturing Industries \& Trades, Technology \& Engineering / Telecommunications},
}
