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Front Matter: Volume 10140

Proceedings of SPIE, Metin N. Gurcan, John E. Tomaszewski
2017 Medical Imaging 2017: Digital Pathology  
using a Base 36 numbering system employing both numerals and letters.  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score [10140-23] 10140 0P Heterogeneity characterization of immunohistochemistry stained tissue using  ... 
doi:10.1117/12.2270372 dblp:conf/midp/X17 fatcat:6yeb63ix6bau7jhipthayjih24

Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

Helge Hecht, Mhd Hasan Sarhan, Vlad Popovici
2020 Applied Sciences  
We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains.  ...  A novel deep autoencoder architecture is proposed for the analysis of histopathology images.  ...  Supplementary Materials: The following are available online at http://www.mdpi.com/2076-3417/10/18/6427/s1, Table S1 : The list of samples from ANHIR collection used in the reported experiments.  ... 
doi:10.3390/app10186427 fatcat:bhrrj54hjfftjncmyb24de2pri

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Yawen Wu, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, Qi Zhu, Jie Zhang, Honghai Hong, Daoqiang Zhang, Kun Huang (+2 others)
2022 Cancers  
Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation.  ...  We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images.  ...  Hematoxylin and eosin (H&E) staining is the most commonly used tissue staining method in the world.  ... 
doi:10.3390/cancers14051199 pmid:35267505 pmcid:PMC8909166 fatcat:7tfcfh4z45goxbcgf23sncok5a

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Nathan Ing, Fangjin Huang, Andrew Conley, Sungyong You, Zhaoxuan Ma, Sergey Klimov, Chisato Ohe, Xiaopu Yuan, Mahul B. Amin, Robert Figlin, Arkadiusz Gertych, Beatrice S. Knudsen
2017 Scientific Reports  
Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture.  ...  Unfortunately, this approach is confounded by the averaging of signals across heterogeneous cell types and across the ternary spatial organization of higher order structures from which the RNA is obtained  ...  Acknowledgements We would like to thank the Cedars-Sinai Biobank for tissue staining. We would also like to thank Dr. Thomas J. Fuchs for valuable technical advice and Drs.  ... 
doi:10.1038/s41598-017-13196-4 pmid:29038551 pmcid:PMC5643431 fatcat:oo3lrxzp2fdnnheetbkwf3vicq

Predicting cell lineages using autoencoders and optimal transport

Karren Dai Yang, Karthik Damodaran, Saradha Venkatachalapathy, Ali C. Soylemezoglu, G. V. Shivashankar, Caroline Uhler, Jian Ma
2020 PLoS Computational Biology  
Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process  ...  Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used  ...  in the heterogeneous tissue microenvironment that are primed for activation.  ... 
doi:10.1371/journal.pcbi.1007828 pmid:32343706 fatcat:kmxgl5g2uneh7nx2hbbhmofasy

The Emergence of Pathomics

Rajarsi Gupta, Tahsin Kurc, Ashish Sharma, Jonas S. Almeida, Joel Saltz
2019 Current Pathobiology Reports  
spectrum of tissues.  ...  Summary WSIs typically contain hundreds of thousands to millions of objects within a heterogeneous histologic landscape.  ...  National Library of Medicine.  ... 
doi:10.1007/s40139-019-00200-x fatcat:2tyb75sicnfhfl5aezkbdu35fa

SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning [article]

Erik A. Burlingame, Mary McDonnell, Geoffrey F. Schau, Guillaume Thibault, Christian Lanciault, Terry Morgan, Brett E. Johnson, Christopher Corless, Joe W. Gray, Young Hwan Chang
2019 bioRxiv   pre-print
In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin-stained tissue as input,  ...  then in near-real time returns inferred virtual immunofluorescence (IF) images that accurately depict the underlying distribution of phenotypes without requiring immunostaining of the tissue being tested  ...  (B) Four heterogeneous samples of H&E-stained PDAC biopsy tissue used in the current study.  ... 
doi:10.1101/730309 fatcat:fxwmzb2nvjenvnn3545ixbw62q

Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications

Yannick Van Herck, Asier Antoranz, Madhavi Dipak Andhari, Giorgia Milli, Oliver Bechter, Frederik De Smet, Francesca Maria Bosisio
2021 Frontiers in Oncology  
In this review, we provide an overview of the state-of-the-art in artificial intelligence and multiplexed immunohistochemistry in pathology, and how these bear the potential to improve diagnostics and  ...  A major asset of in-situ single-cell profiling methods is that these preserve the spatial distribution of the cells in the tissue, allowing researchers to not only determine the cellular composition of  ...  Kucharski et al. 2020 (47) semi-supervised solution using convolutional autoencoders to to segment nests of melanocytes in histopathological images of H&E-stained skin specimens Training set of  ... 
doi:10.3389/fonc.2021.636681 pmid:33854972 pmcid:PMC8040928 fatcat:vqpqkemqp5bpnl6d66k4hmvtkm

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Joel Saltz, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, Kenneth R. Shroyer, Tianhao Zhao, Rebecca Batiste, John Van Arnam, Ilya Shmulevich (+729 others)
2018 Cell Reports  
These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images.  ...  curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized.  ...  Computational Staining uses convolutional neural networks (CNNs) to identify lymphocyte-infiltrated regions in digitized H&E stained tissue specimens.  ... 
doi:10.1016/j.celrep.2018.03.086 pmid:29617659 pmcid:PMC5943714 fatcat:2ypsyvcvyvdezk7tva3dogmcgi

Deep Learning-Enabled Technologies for Bioimage Analysis

Fazle Rabbi, Sajjad Rahmani Dabbagh, Pelin Angin, Ali Kemal Yetisen, Savas Tasoglu
2022 Micromachines  
Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation  ...  Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers.  ...  Traditional DNN and Sparse autoencoders were used to evaluate the performance of the proposed MTDL.  ... 
doi:10.3390/mi13020260 pmid:35208385 pmcid:PMC8880650 fatcat:xbem7lix4nhm7cbaauye46lnye

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.  ...  using autoencoder.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Sandy Napel, Wei Mu, Bruna V. Jardim‐Perassi, Hugo J. W. L. Aerts, Robert J. Gillies
2018 Cancer  
Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity.  ...  An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention  ...  Hence, approaches to characterize and quantify the extent of intratumoral heterogeneity in individual patients might be useful for guiding therapies that adapt during the course of treatment.  ... 
doi:10.1002/cncr.31630 pmid:30383900 pmcid:PMC6482447 fatcat:uqrma7kmxzbwffn3sbanbqupmy

Artificial Intelligence in Cancer Research and Precision Medicine

Bhavneet Bhinder, Coryandar Gilvary, Neel S. Madhukar, Olivier Elemento
2021 Cancer Discovery  
Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects  ...  These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes  ...  Elemento is supported by NIH grants UL1TR002384 and R01CA194547, and the Leukemia and Lymphoma Society Specialized Center of Research grants 180078-02 and 7021-20.  ... 
doi:10.1158/2159-8290.cd-21-0090 pmid:33811123 pmcid:PMC8034385 fatcat:x42n62qmrva2zodrxj4tf7bveq

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Xi Wang, Bin-bin Li
2021 Frontiers in Genetics  
Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis.  ...  Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks.  ...  Subsequently, the DL-extracted imaging features of morphology structure on digitized H&E-stained tissue sections have been used for risk stratification of head and neck tumor patients.  ... 
doi:10.3389/fgene.2021.624820 pmid:33643386 pmcid:PMC7902873 fatcat:eofflp46c5h6pd5gbni7sdnp5u

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer

Panagiotis Barmpoutis, Matthew Di Capite, Hamzeh Kayhanian, William Waddingham, Daniel C. Alexander, Marnix Jansen, Francois Ng Kee Kwong, Luka Brcic
2021 PLoS ONE  
Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS.  ...  TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model.  ...  Acknowledgments We acknowledge the department of Pathology, of UCL Cancer Institute, the UCL Centre for Medical Image Computing and the Department of Histopathology of the Norfolk and Norwich University  ... 
doi:10.1371/journal.pone.0256907 pmid:34555057 pmcid:PMC8460026 fatcat:c6a2gqqqrbeyji2o5khxyjxfs4
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