20.1.3 Medical Applications, Microscope Image Analysis

Zuo, C.[Chao],
Computational phase imaging for light microscopes,
SPIE(Newsroom), November 4, 2015
DOI Link 1511
A combination of optics coding and digital processing enhances the capabilities of traditional light microscopes, enabling acquisition of information such as phase, which cannot otherwise be captured. BibRef

Moser, C.[Christophe], Loterie, D.[Damien],
Digital confocal microscopy through a multimode fiber,
SPIE(Newsroom), October 16, 2015
DOI Link 1511
Compensating for modal scrambling during illumination and detection enables the use of multimode fibers to transmit high-contrast, confocal images for endoscopic applications. BibRef

Cohen, E.A.K., Kim, D., Ober, R.J.,
Cramer-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy,
MedImg(34), No. 12, December 2015, pp. 2632-2644.
IEEE DOI 1601
affine transforms BibRef

Yukihara, E.G., Foiez Ahmed, M.,
Pixel Bleeding Correction in Laser Scanning Luminescence Imaging Demonstrated Using Optically Stimulated Luminescence,
MedImg(34), No. 12, December 2015, pp. 2506-2517.
IEEE DOI 1601
aluminium compounds BibRef

Verrier, N.[Nicolas], Fournier, C.[Corinne], Cazier, A.[Anthony], Fournel, T.[Thierry],
Co-design of an in-line holographic microscope with enhanced axial resolution: Selective filtering digital holography,
JOSA-A(33), No. 1, January 2016, pp. 107-116.
DOI Link 1601
Image reconstruction techniques BibRef

Bostan, E.[Emrah], Froustey, E.[Emmanuel], Nilchian, M., Sage, D.[Daniel], Unser, M.[Michael],
Variational Phase Imaging Using the Transport-of-Intensity Equation,
IP(25), No. 2, February 2016, pp. 807-817.
IEEE DOI 1601
Image reconstruction BibRef

Bostan, E.[Emrah], Froustey, E.[Emmanuel], Rappaz, B.[Benjamin], Shaffer, E.[Etienne], Sage, D.[Daniel], Unser, M.[Michael],
Phase retrieval by using transport-of-intensity equation and differential interference contrast microscopy,
ICIP14(3939-3943)
IEEE DOI 1502
Equations BibRef

Wang, Y.F.[Yun-Feng], Kilpatrick, J.I.[Jason I.], Jarvis, S.P.[Suzanne P.], Boland, F.M.[Francis M.], Kokaram, A.[Anil], Corrigan, D.[David],
Double-Tip Artifact Removal From Atomic Force Microscopy Images,
IP(25), No. 6, June 2016, pp. 2774-2788.
IEEE DOI 1605
Deconvolution BibRef

Yu, Z.X.[Zhi-Xian], Prasad, S.[Sudhakar],
High-numerical-aperture microscopy with a rotating point spread function,
JOSA-A(33), No. 7, July 2016, pp. B58-B69.
DOI Link 1608
Image analysis BibRef

Gopakumar, G., Babu, K.H.[K. Hari], Mishra, D.[Deepak], Gorthi, S.S.[Sai Siva], Subrahmanyam, G.R.K.S.[Gorthi. R. K. Sai],
Cytopathological image analysis using deep-learning networks in microfluidic microscopy,
JOSA-A(34), No. 1, January 2017, pp. 111-121.
DOI Link 1701
Digital image processing BibRef

Yoshida, S.[Shunsuke],
Novel glasses-free tabletop 3D imaging technology for collaborative applications,
SPIE(Newsroom), February 2, 2017
DOI Link 1703
BibRef

Simsek, B.[Burcin], Iyengar, S.[Satish],
On the Distribution of Photon Counts with Censoring in Two-Photon Laser Scanning Microscopy,
JMIV(58), No. 1, May 2017, pp. 47-56.
WWW Link. 1704
BibRef

Kosov, S.[Sergey], Shirahama, K.[Kimiaki], Li, C.[Chen], Grzegorzek, M.[Marcin],
Environmental microorganism classification using conditional random fields and deep convolutional neural networks,
PR(77), 2018, pp. 248-261.
Elsevier DOI 1802
Environmental microorganism, Conditional random fields, Global feature extraction, Image classification, Image segmentation BibRef

Zou, Y., Li, C.[Chen], Shirahama, K.[Kimiaki], Jiang, T., Grzegorzek, M.[Marcin],
Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation,
ICIP16(2475-2479)
IEEE DOI 1610
Feature extraction BibRef

Nguyen, Q.T.[Quoc Thong], Delignon, Y.[Yves], Septier, F.[François], Phan-Ho, A.T.[Anh Thu],
Probabilistic modelling of printed dots at the microscopic scale,
SP:IC(62), 2018, pp. 129-138.
Elsevier DOI 1802
Probabilistic model, Bernoulli process, Metropolis-Hastings within Gibbs, Microscopic printing, Markov chain BibRef

Meiniel, W., Olivo-Marin, J.C., Angelini, E.D.,
Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method,
IP(27), No. 8, August 2018, pp. 3842-3856.
IEEE DOI 1806
Gaussian noise, image denoising, microscopy, Gaussian noise, Poisson noise, biological microscopy images, denoising methods, total variation BibRef

Qin, F., Shen, F., Zhang, D., Liu, X., Xu, D.,
Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement,
SMCS(48), No. 8, August 2018, pp. 1348-1359.
IEEE DOI 1808
Feature extraction, Microscopy, Cameras, Image edge detection, Manipulators, Machine vision, Robustness, Geometric constraint, precision assembly BibRef

Han, L.[Liang], Yin, Z.Z.[Zhao-Zheng],
Learning to transfer microscopy image modalities,
MVA(29), No. 8, November 2018, pp. 1257-1267.
WWW Link. 1811
BibRef

Kuniyoshi, F.[Fusataka], Funatomi, T.[Takuya], Kubo, H.[Hiroyuki], Sawada, Y.[Yoshihide], Kato, Y.O.[Yumiko O.], Mukaigawa, Y.[Yasuhiro],
Visibility Enhancement by Integrating Refocusing and Direct-Global Separation with Contact Imaging,
IJCV(127), No. 8, August 2019, pp. 1162-1174.
Springer DOI 1907
Compact lensless microscopy technique for living cells. BibRef

Chouzenoux, E.[Emilie], Lau, T.T.K.[Tim Tsz-Kit], Lefort, C.[Claire], Pesquet, J.C.[Jean-Christophe],
Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging,
JMIV(61), No. 7, September 2019, pp. 1037-1050.
Springer DOI 1908
BibRef

Kim, D.W.[Dae Woo], Aguilar, C., Zhao, H., Comer, M.L.[Mary L.],
Narrow Gap Detection in Microscope Images Using Marked Point Process Modeling,
IP(28), No. 10, October 2019, pp. 5064-5076.
IEEE DOI 1909
BibRef
Earlier: A1, A4, Only:
Channel detection in microscope images of materials using marked point process modeling,
ICIP15(3054-3058)
IEEE DOI 1512
Image segmentation, Microscopy, Channel models, Shape, Markov processes, Task analysis, Monte Carlo methods, MPP, segmentation. Channel detection; Marked Point Process; Segmentation BibRef

Sintorn, M., Bischof, L., Jackway, P., Haggarty, S., Buckley, M.,
Gradient based intensity normalization,
J. Microsc(240), 2010, pp. 249-258.
DOI Link BibRef 1000

Dietlmeier, J.[Julia], McGuinness, K.[Kevin], Rugonyi, S.[Sandra], Wilson, T.[Teresa], Nuttall, A.[Alfred], O'Connor, N.E.[Noel E.],
Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data,
PRL(128), 2019, pp. 521-528.
Elsevier DOI 1912
Deep learning, Few-shot learning, Cardiac and outer hair cells, Gradient boosting, Mitochondria segmentation, Transfer learning BibRef

Sheppard, C.J.R.[Colin J. R.],
Partially coherent microscope imaging system in phase space: Effect of defocus and phase reconstruction,
JOSA-A(35), No. 11, November 2018, pp. 1846-1854.
DOI Link 1912
Paraxial wave optics, Microscopy, Optical transfer functions, Partial coherence in imaging, X-ray imaging, Spatial frequency BibRef

Mehta, S.B.[Shalin B.], Sheppard, C.J.R.[Colin J. R.],
Partially coherent microscope in phase space,
JOSA-A(35), No. 8, August 2018, pp. 1272-1282.
DOI Link 1912
Paraxial wave optics , Microscopy, Optical transfer functions, Partial coherence in imaging, X-ray imaging, Spatial frequency BibRef

Usmani, K.[Kashif], Ahmad, A.[Azeem], Joshi, R.[Rakesh], Dubey, V.[Vishesh], Butola, A.[Ankit], Mehta, D.S.[Dalip Singh],
Relationship between the source size at the diffuser plane and the longitudinal spatial coherence function of the optical coherence microscopy system,
JOSA-A(36), No. 12, December 2019, pp. D41-D46.
DOI Link 1912
Coherence theory, Laser sources, Light sources, Optical fields, Spatial frequency, White light BibRef

Zhang, L.G.[Li-Guo], Yin, G.S.[Gui-Sheng], Han, Q.L.[Qi-Long], Sun, J.G.[Jian-Guo],
Wide-field and full-focus optical microscopic imaging system,
JOSA-A(36), No. 6, June 2019, pp. 950-963.
DOI Link 1912
Digital image processing, Image fusion, Image metrics, Image processing, Image quality, Medical image processing BibRef

Francis, B.[Bibin], Mathew, M.[Manoj], Arigovindan, M.[Muthuvel],
Multiresolution-based weighted regularization for denoised image interpolation from scattered samples with application to confocal microscopy,
JOSA-A(35), No. 10, October 2018, pp. 1749-1759.
DOI Link 1912
Image reconstruction-restoration, Fluorescence microscopy, Image reconstruction techniques, Inverse problems BibRef

Sentenac, A.[Anne], Mertz, J.[Jerome],
Unified description of three-dimensional optical diffraction microscopy: from transmission microscopy to optical coherence tomography: tutorial,
JOSA-A(35), No. 5, May 2018, pp. 748-754.
DOI Link 1912
Imaging systems, Three-dimensional microscopy, Confocal microscopy, Light fields, Phase imaging, Three dimensional imaging BibRef

Mobiny, A., Lu, H., Nguyen, H.V., Roysam, B., Varadarajan, N.,
Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network,
MedImg(39), No. 1, January 2020, pp. 1-10.
IEEE DOI 2001
Routing, Task analysis, Training, Microscopy, Face, Feature extraction, Pediatrics, Apoptosis, capsule network, cell classification BibRef

Hosseini, M.S., Brawley-Hayes, J.A.Z., Zhang, Y., Chan, L., Plataniotis, K.N., Damaskinos, S.,
Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology,
MedImg(39), No. 1, January 2020, pp. 62-74.
IEEE DOI 2001
Pathology, Measurement, Kernel, Quality assessment, Feature extraction, Microscopy, MaxPol derivative library BibRef

de Haan, K., Rivenson, Y., Wu, Y., Ozcan, A.,
Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy,
PIEEE(108), No. 1, January 2020, pp. 30-50.
IEEE DOI 2001
Deep learning, Biomedical imaging, Optical imaging, Microscopy, Image reconstruction, Machine learning, Biomedical imaging, deep learning BibRef

Liu, W.Q.[Wen-Qian], Li, W.H.[Wei-Hong], Gong, W.G.[Wei-Guo],
Ensemble of fine-tuned convolutional neural networks for urine sediment microscopic image classification,
IET-CV(14), No. 1, February 2020, pp. 18-25.
DOI Link 2002
BibRef

Yamaguchi, T.[Takahiro], Nagahara, H.[Hajime], Morooka, K.[Ken'ichi], Nakashima, Y.[Yuta], Uranishi, Y.[Yuki], Miyauchi, S.[Shoko], Kurazume, R.[Ryo],
3d Image Reconstruction from Multi-focus Microscopic Images,
PSIVT19(73-85).
Springer DOI 2003
BibRef

Loewke, N.O., Qiu, Z., Mandella, M.J., Ertsey, R., Loewke, A., Gunaydin, L.A., Rosenthal, E.L., Contag, C.H., Solgaard, O.,
Software-Based Phase Control, Video-Rate Imaging, and Real-Time Mosaicing With a Lissajous-Scanned Confocal Microscope,
MedImg(39), No. 4, April 2020, pp. 1127-1137.
IEEE DOI 2004
Phase control, Microscopy, Spatial resolution, Real-time systems, Mirrors, Confocal microscopy, Lissajous imaging, mosaicing, software-based phase control BibRef

Shajkofci, A., Liebling, M.,
Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy,
IP(29), 2020, pp. 5848-5861.
IEEE DOI 2005
BibRef
Earlier:
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks,
ICIP18(3818-3822)
IEEE DOI 1809
Microscopy, Optical imaging, Optical diffraction, Deconvolution, Estimation, Optical microscopy, Calibration, depth from focus. Deconvolution, Optical imaging, Training, Integrated optics, Optical diffraction, convolutional neural networks BibRef

Cardoen, B., Yedder, H.B., Sharma, A., Chou, K.C., Nabi, I.R., Hamarneh, G.,
ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy,
MedImg(39), No. 6, June 2020, pp. 1942-1956.
IEEE DOI 2006
Image reconstruction, Microscopy, Proteins, Estimation, Labeling, astigmatism BibRef

Jirik, M.[Miroslav], Moulisova, V.[Vladimira], Schindler, C.[Claudia], Cervenkova, L.[Lenka], Palek, R.[Richard], Rosendorf, J.[Jachym], Arlt, J.[Janine], Bolek, L.[Lukas], Dejmek, J.[Jiri], Dahmen, U.[Uta], Jirikova, K.[Kamila], Gruber, I.[Ivan], Liska, V.[Vaclav], Zelezny, M.[Milos],
Micrant: Towards Regression Task Oriented Annotation Tool for Microscopic Images,
IWCIA20(209-218).
Springer DOI 2009
BibRef

Liu, D., Zhang, D., Song, Y., Zhang, F., O'Donnell, L., Huang, H., Chen, M., Cai, W.,
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images,
MedImg(40), No. 1, January 2021, pp. 154-165.
IEEE DOI 2012
Image segmentation, Semantics, Task analysis, Feature extraction, Microscopy, Training, Adaptation models, microscopy images BibRef

Nehme, E.[Elias], Ferdman, B.[Boris], Weiss, L.E.[Lucien E.], Naor, T.[Tal], Freedman, D.[Daniel], Michaeli, T.[Tomer], Shechtman, Y.[Yoav],
Learning Optimal Wavefront Shaping for Multi-Channel Imaging,
PAMI(43), No. 7, July 2021, pp. 2179-2192.
IEEE DOI 2106
Imaging, Microscopy, Location awareness, Optical microscopy, Optical imaging, end-to-end optimization BibRef

Li, A.C.[An-Cin], Vyas, S.I.[Sun-Il], Lin, Y.H.[Yu-Hsiang], Huang, Y.Y.[Yi-You], Huang, H.M.[Hsuan-Ming], Luo, Y.[Yuan],
Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging,
MedImg(40), No. 11, November 2021, pp. 3229-3237.
IEEE DOI 2111
Microscopy, Transfer functions, Imaging, Phase measurement, Image reconstruction, Biomedical measurement, CycleGAN BibRef

Huang, L.[Ling], Cheng, D.[Deruo], Yang, X.[Xulei], Lin, T.[Tong], Shi, Y.Q.[Yi-Qiong], Yang, K.[Kaiyi], Gwee, B.H.[Bah Hwee], Wen, B.[Bihan],
Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning,
ICIP21(3497-3501)
IEEE DOI 2201
Deep learning, Training, Shape, Microscopy, Manufacturing, Labeling, Microstructure, Microscopy, anomaly detection, inpainting, real-world dataset BibRef

Arbelle, A.[Assaf], Cohen, S.[Shaked], Raviv, T.R.[Tammy Riklin],
Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos,
MedImg(41), No. 8, August 2022, pp. 1948-1960.
IEEE DOI 2208
Computer architecture, Microprocessors, Image segmentation, Annotations, Microscopy, Training, Deep learning, image sequences, object segmentation BibRef

Xie, Y.C.[Yao-Chen], Ding, Y.[Yu], Ji, S.W.[Shui-Wang],
Augmented Equivariant Attention Networks for Microscopy Image Transformation,
MedImg(41), No. 11, November 2022, pp. 3194-3206.
IEEE DOI 2211
Computational modeling, Training, Deep learning, Superresolution, Task analysis, Electron microscopy, Neural networks, Deep learning, image transformation BibRef

Wang, Y.K.[Yu-Kun], Gu, Y.F.[Yan-Feng], Li, X.[Xiaomei],
A Novel Low Rank Smooth Flat-Field Correction Algorithm for Hyperspectral Microscopy Imaging,
MedImg(41), No. 12, December 2022, pp. 3862-3872.
IEEE DOI 2212
Microscopy, Hyperspectral imaging, Imaging, Pathology, Optical microscopy, Optical imaging, Adaptive optics, vignetting BibRef

Yang, Y.[Yang], Tu, Y.[Yanlun], Lei, H.[Houchao], Long, W.[Wei],
HAMIL: Hierarchical aggregation-based multi-instance learning for microscopy image classification,
PR(136), 2023, pp. 109245.
Elsevier DOI 2301
Multi-instance learning, Biomedical image, Hierarchical aggregation BibRef

Courbot, J.B.[Jean-Baptiste], Colicchio, B.[Bruno],
Transformed Gaussian Random Fields for Unsupervised Image Deconvolution,
SPLetters(29), 2022, pp. 2702-2706.
IEEE DOI 2301
Solid modeling, Deconvolution, Monte Carlo methods, Computational modeling, Microscopy, Numerical models, Hamiltonian Monte Carlo BibRef

di Marco, N.[Niccolò], Frosini, A.[Andrea],
The Generalized Microscopic Image Reconstruction Problem for Hypergraphs,
IWCIA22(317-331).
Springer DOI 2301
BibRef

Shats, D.[Daniel], Hezi, H.[Hadar], Shani, G.[Guy], Maruvka, Y.E.[Yosef E.], Freiman, M.[Moti],
Patient-level Microsatellite Stability Assessment from Whole Slide Images by Combining Momentum Contrast Learning and Group Patch Embeddings,
MCV22(454-465).
Springer DOI 2304
BibRef

Liu, Y.[Yi], Caplan, J.[Jeffrey], Kambhamettu, C.[Chandra],
Extraction and Quantification of Actin Cytoskeleton in Microscopic Images Using a Deep Learning Based Framework and a Curve Clustering Model,
ICPR22(4270-4276)
IEEE DOI 2212
Deep learning, Histograms, Shape, Microscopy, Pipelines, Organizations, Feature extraction BibRef

Yayci, Z.O.[Zeynep Ovgu], Dura, U.[Ugur], Kaya, Z.B.[Zeynep Betul], Cetin, A.E.[Arif E.], Turkan, M.[Mehmet],
Microscale Image Enhancement Via PCA and Well-Exposedness Maps,
ICIP22(2092-2096)
IEEE DOI 2211
Histograms, Visualization, Laplace equations, Image color analysis, Microscopy, Lighting, Colored noise, Microscale image enhancement, principle component analysis BibRef

Chattopadhyay, S., Malachowski, A., Swain, J.K., Dalmo, R.A., Horsch, A., Prasad, D.K.,
Mapping Functional Changes in the Embryonic Heart of Atlantic Salmon Post Viral Infection Using AI Technique,
ICIP22(3101-3105)
IEEE DOI 2211
Heart, Perturbation methods, Microscopy, Heuristic algorithms, Fish, Salmon fish, Embryonic heart BibRef

Pedraza, A.[Anibal], Ruiz-Santaquiteria, J.[Jesus], Deniz, O.[Oscar], Bueno, G.[Gloria],
Parasitic Egg Detection and Classification with Transformer-Based Architectures,
ICIP22(4301-4305)
IEEE DOI 2211
Microscopy, Object detection, Medical services, Transformers, Object recognition, Task analysis, Deep Learning, Object Detection, Parasitic Eggs BibRef

Pratama, Y.[Yohanssen], Fujimura, Y.[Yuki], Funatomi, T.[Takuya], Mukaigawa, Y.[Yasuhiro],
Parasitic Egg Detection and Classification by Utilizing the YOLO Algorithm with Deep Latent Space Image Restoration and GrabCut Augmentation,
ICIP22(4311-4315)
IEEE DOI 2211
Image resolution, Microscopy, Lighting, Object detection, Detectors, Classification algorithms, Image restoration, Classification, YOLO BibRef

Ruiz-Santaquiteria, J.[Jesus], Pedraza, A.[Anibal], Vallez, N.[Noelia], Velasco, A.[Alberto],
Parasitic Egg Detection with a Deep Learning Ensemble,
ICIP22(4283-4286)
IEEE DOI 2211
Deep learning, Object detection, Medical services, Manuals, Task analysis, Parasitic Eggs, Object Detection, Neural networks, Deep Learning BibRef

Aung, Z.H.[Zaw Htet], Srithaworn, K.[Kittinan], Achakulvisut, T.[Titipat],
Multitask learning via pseudo-label generation and ensemble prediction for parasitic egg cell detection: IEEE ICIP Challenge 2022,
ICIP22(4273-4277)
IEEE DOI 2211
Training, Representation learning, Head, Computational modeling, Parasitic diseases, Training data, Object detection, parasitic egg BibRef

Tureckova, A.[Alzbeta], Turecek, T.[Tomas], Oplatkova, Z.K.[Zuzana Kominkova],
ICIP 2022 Challenge: PEDCMI, TOOD Enhanced by Slicing-Aided Fine-Tuning and Inference,
ICIP22(4292-4295)
IEEE DOI 2211
Deep learning, Training, Analytical models, Data analysis, Image resolution, Microscopy, Pipelines, Deep Learning Pipeline, Slicing Aided Inference BibRef

AlDahoul, N.[Nouar], Karim, H.A.[Hezerul Abdul], Kee, S.L.[Shaira Limson], Tan, M.J.T.[Myles Joshua Toledo],
Localization and Classification of Parasitic Eggs in Microscpic Images Using An Efficientdet Detector,
ICIP22(4253-4257)
IEEE DOI 2211
Location awareness, Training, Microscopy, Detectors, Pattern recognition, EfficientDet, microscopic image, parasitic egg BibRef

Wang, Y.Q.[Yu-Qi], He, Z.Q.[Zhi-Qiang], Huang, S.H.[Sheng-Hui], Du, H.B.[Hua-Bin],
A Robust Ensemble Model For Parasitic Egg Detection And Classification,
ICIP22(4258-4262)
IEEE DOI 2211
Deep learning, Biological system modeling, Microscopy, Transfer learning, Interference, Feature extraction, Robustness, robustness BibRef

Anantrasirichai, N.[Nantheera], Chalidabhongse, T.H.[Thanarat H.], Palasuwan, D.[Duangdao], Naruenatthanaset, K.[Korranat], Kobchaisawat, T.[Thananop], Nunthanasup, N.[Nuntiporn], Boonpeng, K.[Kanyarat], Ma, X.D.[Xu-Dong], Achim, A.[Alin],
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results,
ICIP22(4306-4310)
IEEE DOI 2211
Deep learning, Image color analysis, Microscopy, Brightness, Object detection, Manuals, Colored noise, parasitic egg, deep learning BibRef

Zhang, H.[Hongrun], Meng, Y.[Yanda], Zhao, Y.T.[Yi-Tian], Qiao, Y.H.[Yi-Hong], Yang, X.Y.[Xiao-Yun], Coupland, S.E.[Sarah E.], Zheng, Y.L.[Ya-Lin],
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification,
CVPR22(18780-18790)
IEEE DOI 2210
Measurement, Image resolution, Histopathology, Lung cancer, Feature extraction, Pattern recognition, Medical, Self- semi- meta- unsupervised learning BibRef

Chen, R.J.[Richard J.], Lu, M.Y.[Ming Y.], Weng, W.H.[Wei-Hung], Chen, T.Y.[Tiffany Y.], Williamson, D.F.[Drew FK.], Manz, T.[Trevor], Shady, M.[Maha], Mahmood, F.[Faisal],
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images,
ICCV21(3995-4005)
IEEE DOI 2203
Representation learning, Visualization, Histopathology, Genomics, Predictive models, Transformers, Biological information theory, Vision + other modalities BibRef

Albuquerque, T.[Tomé], Moreira, A.[Ana], Cardoso, J.S.[Jaime S.],
Deep Ordinal Focus Assessment for Whole Slide Images,
CDPath21(657-663)
IEEE DOI 2112
Training, Measurement, Image quality, Decision support systems, Manufacturing processes, Databases, Computational modeling BibRef

Theelke, L.[Luisa], Wilm, F.[Frauke], Marzahl, C.[Christian], Bertram, C.A.[Christof A.], Klopfleisch, R.[Robert], Maier, A.[Andreas], Aubreville, M.[Marc], Breininger, K.[Katharina],
Iterative Cross-Scanner Registration for Whole Slide Images,
CDPath21(582-590)
IEEE DOI 2112
Pathology, Image resolution, Microscopy, Estimation, Registers BibRef

Yang, K.[Karren], Goldman, S.[Samuel], Jin, W.[Wengong], Lu, A.X.[Alex X.], Barzilay, R.[Regina], Jaakkola, T.[Tommi], Uhler, C.[Caroline],
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis,
CVPR21(6684-6694)
IEEE DOI 2111
Training, Measurement, Image resolution, Image synthesis, Microprocessors, Biological system modeling, Microscopy BibRef

Li, B.[Bin], Li, Y.[Yin], Eliceiri, K.W.[Kevin W.],
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning,
CVPR21(14313-14323)
IEEE DOI 2111
Training, Location awareness, Image resolution, Annotations, Feature extraction, Distance measurement, Pattern recognition BibRef

Venkataramanan, A.[Aishwarya], Laviale, M.[Martin], Figus, C.[Cécile], Usseglio-Polatera, P.[Philippe], Pradalier, C.[Cédric],
Tackling Inter-class Similarity and Intra-class Variance for Microscopic Image-Based Classification,
CVS21(93-103).
Springer DOI 2109
BibRef

Shrivastava, A.[Aman], Adorno, W.[William], Sharma, Y.[Yash], Ehsan, L.[Lubaina], Ali, S.A.[S. Asad], Moore, S.R.[Sean R.], Amadi, B.[Beatrice], Kelly, P.[Paul], Syed, S.[Sana], Brown, D.E.[Donald E.],
Self-attentive Adversarial Stain Normalization,
AIDP20(120-140).
Springer DOI 2103
Stained biopsy images. BibRef

Zhang, Q.C.[Qing-Chao], Heldermon, C.D.[Coy D.], Toler-Franklin, C.[Corey],
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans,
ISVC20(II:139-153).
Springer DOI 2103
BibRef

Page, J., Favaros, P.,
Learning to Model and Calibrate Optics Via a Differentiable Wave Optics Simulator,
ICIP20(2995-2999)
IEEE DOI 2011
Microscopy, Optical diffraction, Optical imaging, Adaptive optics, Optical sensors, Computational modeling, Lenses, PSF engineering, differentiable simulator BibRef

Aktar, R., Huxley, V.H., Guidoboni, G., Ali Akbarpour, H., Bunyak, F., Palaniappan, K.,
Mosaicing of Dynamic Mesentery Video with Gradient Blending,
ICIP20(563-567)
IEEE DOI 2011
Correlation, Robustness, Microscopy, Feature extraction, Biomedical imaging, Image edge detection, Image registration, Gradient blending BibRef

Sandhan, T., Choi, J.Y.,
Separating Particulate Matter From a Single Microscopic Image,
CVPR20(4583-4592)
IEEE DOI 2008
Microscopy, Glass, Optical microscopy, Visualization, Diffraction, Lenses BibRef

Miolane, N.[Nina], Holmes, S.,
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders,
CVPR20(14491-14499)
IEEE DOI 2008
Manifolds, Data models, Principal component analysis, Task analysis, Probabilistic logic, Biomedical imaging, Uncertainty BibRef

Miolane, N.[Nina], Poitevin, F., Li, Y., Holmes, S.,
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks,
Microscopy20(4174-4183)
IEEE DOI 2008
Space vehicles, Orbits, Cameras, Estimation, Image reconstruction BibRef

Weigert, M., Schmidt, U., Haase, R., Sugawara, K., Myers, G.,
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy,
WACV20(3655-3662)
IEEE DOI 2006
Shape, Microscopy, Image segmentation, Anisotropic magnetoresistance, Training BibRef

Khan, S.S.[Salman Siddique], Adarsh, V.R., Boominathan, V.[Vivek], Tan, J.[Jasper], Veeraraghavan, A.[Ashok], Mitra, K.[Kaushik],
Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images,
ICCV19(7859-7868)
IEEE DOI 2004
cameras, image reconstruction, image sensors, neural nets, optical microscopes, miniature cameras, distributed monitoring, Lenses BibRef

Singh, H.[Harbinder], Sánchez, C.[Carlos], Cristóbal, G.[Gabriel], Bueno, G.[Gloria],
Pencil Drawing of Microscopic Images Through Edge Preserving Filtering,
IbPRIA19(II:189-200).
Springer DOI 1910
BibRef

Forero, M.G.[Manuel G.], Arias-Rubio, C.[Carlos], Horta-Júnior, J.D.C.[José De_Anchieta C.], López, D.E.[Dolores E.],
A Note on Gradient-Based Intensity Normalization,
IbPRIA19(I:161-169).
Springer DOI 1910
Builds on work of Sintorn.
See also Gradient based intensity normalization. BibRef

Tran, D.H.[Duc Hoa], Meunier, M.[Michel], Cheriet, F.[Farida],
WaveM-CNN for Automatic Recognition of Sub-cellular Organelles,
ICIAR19(I:186-194).
Springer DOI 1909
BibRef

Zafari, S.[Sahar], Eerola, T.[Tuomas], Ferreira, P.[Paulo], Kälviäinen, H.[Heikki], Bovik, A.[Alan],
Automated Segmentation of Nanoparticles in BF TEM Images by U-Net Binarization and Branch and Bound,
CAIP19(I:113-125).
Springer DOI 1909
BibRef

Bhugra, S.[Swati], Mishra, D.[Deepak], Anupama, A.[Anupama], Chaudhury, S.[Santanu], Lall, B.[Brejesh], Chugh, A.[Archana], Chinnusamy, V.[Viswanathan],
Deep Convolutional Neural Networks Based Framework for Estimation of Stomata Density and Structure from Microscopic Images,
BioIm18(VI:412-423).
Springer DOI 1905
BibRef

Basu, S., Rexhepaj, E., Spassky, N., Genovesio, A., Paulsen, R.R., Shihavuddin, A.S.M.,
FastSME: Faster and Smoother Manifold Extraction from 3D Stack,
Microscopy18(2362-23628)
IEEE DOI 1812
Manifolds, Indexes, Biology, Microscopy, Optimization BibRef

Aziz, A., Pande, H., Cheluvaraju, B., Dastidar, T.R.,
Improved Extraction of Objects from Urine Microscopy Images with Unsupervised Thresholding and Supervised U-net Techniques,
Microscopy18(2311-23118)
IEEE DOI 1812
Image segmentation, Microscopy, Image edge detection, Shape, Transforms, Microorganisms BibRef

Levis, A., Schechner, Y.Y., Talmon, R.,
Statistical Tomography of Microscopic Life,
CVPR18(6411-6420)
IEEE DOI 1812
Microscopy, Tomography, Estimation, Organisms BibRef

Zhang, T., Carvajal, J., Smith, D.F., Zhao, K., Wiliem, A., Hobson, P., Jennings, A., Lovell, B.C.,
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks,
ICPR18(2314-2319)
IEEE DOI 1812
Task analysis, Quality assessment, Microscopy, Neural networks, Microorganisms, Image analysis, Support vector machines BibRef

Yang, C., Bu, X., Ma, H., Zhang, L., Cao, X., Yue, T., Hua, X., Yan, F.,
Resolution-Enhanced Lensless Color Shadow Imaging Microscopy Based on Large Field-of-View Submicron-Pixel Imaging Sensors,
Microscopy18(2327-23277)
IEEE DOI 1812
Microscopy, Sensors, Image sensors, Spatial resolution, Lighting BibRef

Chen, X., Xie, Q., Shen, L., Han, H.,
Morphology-Retained Non-Linear Image Registration of Serial Electron Microscopy Sections,
ICIP18(3833-3837)
IEEE DOI 1809
Image reconstruction, Biological tissues, Image registration, Reliability, Scanning electron microscopy, Image registration, correspondence extraction BibRef

Liu, D.N.[Dong-Nan], Zhang, D.H.[Dong-Hao], Song, Y.[Yang], Zhang, C.Y.[Chao-Yi], Huang, H.[Heng], Chen, M.[Mei], Cai, W.D.[Wei-Dong],
Large Kernel Refine Fusion Net for Neuron Membrane Segmentation,
Microscopy18(2293-22938)
IEEE DOI 1812
Kernel, Image segmentation, Task analysis, Image resolution, Neurons, Decoding, Biomembranes BibRef

Liu, D.N.[Dong-Nan], Zhang, D.H.[Dong-Hao], Liu, S.Q.[Si-Qi], Song, Y.[Yang], Jia, H.Z.[Hao-Zhe], Feng, D.D.[David Dagan], Xia, Y.[Yong], Cai, W.D.[Wei-Dong],
Densely Connected Large Kernel Convolutional Network for Semantic Membrane Segmentation in Microscopy Images,
ICIP18(2461-2465)
IEEE DOI 1809
Kernel, Image segmentation, Decoding, Neurons, Image resolution, Semantics, Microscopy, neuronal boundary segmentation, deep neural network BibRef

Luo, Y., Andersson, S.B.,
Sampling pattern design algorithm for atomic force microscopy images,
ICIP17(2109-2113)
IEEE DOI 1803
Force, Image reconstruction, Imaging, Indexes, Matching pursuit algorithms, Surface topography, sampling pattern design BibRef

Cheng, H.C., Cardone, A., Varshney, A.,
Interactive exploration of microstructural features in gigapixel microscopy images,
ICIP17(335-339)
IEEE DOI 1803
Image color analysis, Image resolution, Image segmentation, Intestines, Microscopy, Muscles, Visualization, Image segmentation, gigapixel images BibRef

Saponaro, P., Treible, W., Kolagunda, A., Rhein, S., Caplan, J., Kambhamettu, C., Wisser, R.,
Three-dimensional segmentation of vesicular networks of fungal hyphae in macroscopic microscopy image stacks,
ICIP17(3285-3289)
IEEE DOI 1803
Feature extraction, Generators, Image edge detection, Image segmentation, Microscopy, Skeleton, Fungal Hyphae, Skeletonization BibRef

Sadanandan, S.K.[Sajith Kecheril], Karlsson, J.[Johan], Wählby, C.[Carolina],
Spheroid Segmentation Using Multiscale Deep Adversarial Networks,
BioIm17(36-41)
IEEE DOI 1802
Convolution, Feature extraction, Image segmentation, Neural networks, Shape, Training BibRef

Hast, A.[Anders], Kylberg, G.[Gustaf], Sintorn, I.M.[Ida-Maria],
An Efficient Descriptor Based on Radial Line Integration for Fast Non-invariant Matching and Registration of Microscopy Images,
ACIVS17(723-734).
Springer DOI 1712
BibRef

Han, L.[Liang], Yin, Z.Z.[Zhao-Zheng],
Transferring Microscopy Image Modalities with Conditional Generative Adversarial Networks,
Microscopy17(851-859)
IEEE DOI 1709
Biology, Generators, Image segmentation, Interference, Microscopy, Visualization BibRef

Saponaro, P., Treible, W., Kolagunda, A., Chaya, T., Caplan, J., Kambhamettu, C., Wisser, R.,
DeepXScope: Segmenting Microscopy Images with a Deep Neural Network,
Microscopy17(843-850)
IEEE DOI 1709
Computer architecture, Image segmentation, Measurement, Microscopy, Training, Training, data BibRef

Bao, J., Fan, J., Hu, X., Wang, J., Wang, L.,
An effective consistency correction and blending method for camera-array-based microscopy imaging,
WSSIP17(1-5)
IEEE DOI 1707
Cameras, Distribution functions, Lenses, Lighting, Mathematical model, Microscopy, Camera Array, Consistency Correction, Illumination Compensation, Improved Alpha Blending, Response Function, Vignetting, Compensation BibRef