research-article

Biological interpretation of morphological patterns in histopathological whole-slide images

Authors:

Adeboye O. Osunkoya,

May D. WangAuthors Info & Claims

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

Pages 218 - 225

https://doi.org/10.1145/2382936.2382964

Published: 07 October 2012 Publication History

Abstract

We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.

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Cited By

Singh SJaiswal AKumar R(2024)Feature Selection and Reduction for Analysis of Histopathology ImagesProceedings of the 12th International Conference on Soft Computing for Problem Solving10.1007/978-981-97-3292-0_42(601-612)Online publication date: 1-Jul-2024
https://doi.org/10.1007/978-981-97-3292-0_42
Araújo Ada Silva VKudo Mde Souza ESaldivia‐Siracusa CGiraldo‐Roldán DLopes MVargas PKhurram SPearson AKowalski Lde Carvalho ASantos‐Silva AMoraes M(2023)Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approachJournal of Oral Pathology & Medicine10.1111/jop.1339752:2(109-118)Online publication date: 4-Jan-2023
https://doi.org/10.1111/jop.13397
Breen JAllen KZucker KAdusumilli PScarsbrook AHall GOrsi NRavikumar N(2023)Artificial intelligence in ovarian cancer histopathology: a systematic reviewnpj Precision Oncology10.1038/s41698-023-00432-67:1Online publication date: 31-Aug-2023
https://doi.org/10.1038/s41698-023-00432-6
Show More Cited By

Index Terms

Biological interpretation of morphological patterns in histopathological whole-slide images
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Shape representations

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Published In

cover image ACM Conferences

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

October 2012

725 pages

ISBN:9781450316705

DOI:10.1145/2382936

General Chair:
Sanjay Ranka
University of Florida
,
Program Chairs:
Tamer Kahveci
University of Florida
,
Mona Singh
Princeton University

Copyright © 2012 ACM.

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

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Published: 07 October 2012

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BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

October 7 - 10, 2012

Florida, Orlando

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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Singh SJaiswal AKumar R(2024)Feature Selection and Reduction for Analysis of Histopathology ImagesProceedings of the 12th International Conference on Soft Computing for Problem Solving10.1007/978-981-97-3292-0_42(601-612)Online publication date: 1-Jul-2024
https://doi.org/10.1007/978-981-97-3292-0_42
Araújo Ada Silva VKudo Mde Souza ESaldivia‐Siracusa CGiraldo‐Roldán DLopes MVargas PKhurram SPearson AKowalski Lde Carvalho ASantos‐Silva AMoraes M(2023)Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approachJournal of Oral Pathology & Medicine10.1111/jop.1339752:2(109-118)Online publication date: 4-Jan-2023
https://doi.org/10.1111/jop.13397
Breen JAllen KZucker KAdusumilli PScarsbrook AHall GOrsi NRavikumar N(2023)Artificial intelligence in ovarian cancer histopathology: a systematic reviewnpj Precision Oncology10.1038/s41698-023-00432-67:1Online publication date: 31-Aug-2023
https://doi.org/10.1038/s41698-023-00432-6
Moraes AMoreno MRibeiro RFerreira P(2023)Predicting Age from Human Lung Tissue Through Multi-modal Data IntegrationDiscovery Science10.1007/978-3-031-45275-8_43(644-658)Online publication date: 8-Oct-2023
https://doi.org/10.1007/978-3-031-45275-8_43
Sun BLaberiano-Fernández CSalazar-Alejo RZhang JSolorzano Rendon JLee JSolis Soto LWistuba IParra E(2022)Impact of Region-of-Interest Size on Immune Profiling Using Multiplex Immunofluorescence Tyramide Signal Amplification for Paraffin-Embedded Tumor TissuesPathobiology10.1159/00052375190:1(1-12)Online publication date: 24-May-2022
https://doi.org/10.1159/000523751
Li CZhu XYao JHuang J(2022)Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956296(4256-4262)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956296
Li XLi CRahaman MSun HLi XWu JYao YGrzegorzek M(2022)A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approachesArtificial Intelligence Review10.1007/s10462-021-10121-055:6(4809-4878)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10462-021-10121-0
Huang YXue YLan JDeng YChen GZhang HDang MTong T(2021)Deep Learning Framework for Detecting Positive Lymph Nodes of Gastric Cancer on Histopathological ImagesProceedings of the 2021 6th International Conference on Biomedical Imaging, Signal Processing10.1145/3502803.3502806(14-23)Online publication date: 29-Oct-2021
https://dl.acm.org/doi/10.1145/3502803.3502806
Shi WTong LZhu YWang M(2021)COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural NetworksIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2021.307489325:7(2376-2387)Online publication date: Jul-2021
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Xing FZhang XCornish T(2021)Artificial intelligence for pathologyArtificial Intelligence in Medicine10.1016/B978-0-12-821259-2.00011-9(183-221)Online publication date: 2021
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