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Front Matter: Volume 10140
2017
Medical Imaging 2017: Digital Pathology
Preserved Fulltext
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
2020
Applied Sciences
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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
2022
Cancers
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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
2017
Scientific Reports
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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
2020
PLoS Computational Biology
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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
2019
Current Pathobiology Reports
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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]
2019
bioRxiv
pre-print
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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
2021
Frontiers in Oncology
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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
2018
Cell Reports
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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
2022
Micromachines
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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]
2019
arXiv
pre-print
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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
2018
Cancer
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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
2021
Cancer Discovery
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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
2021
Frontiers in Genetics
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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
2021
PLoS ONE
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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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