Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images.
Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for m...
| Publicado en: | Biomedical Informatics Insights no. 10; pp. 1 - 2 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134199459&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134199459 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2018 iid: 10 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 134199459 134199459 134199459 10.1177/1178222618807481 134199459 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images. aug: au: Srivastava, Arunima Kulkarni, Chaitanya Huang, Kun Parwani, Anil Mallick, Parag Machiraju, Raghu affil: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA sug: subj: Histology Slides Evaluation Pathologists Neural Networks (Computer) Models, Biological Image Interpretation, Computer Assisted Quality Improvement Human Workflow Neoplasms by Histologic Type Clinical Data Repository Disease Attributes Breast Neoplasms Pathology ab: Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). We synthesize a workflow which (a) intelligently samples the training data by automatically selecting only image areas that display visible disease-relevant tissue state and (b) isolates regions most pertinent to the trained CNN prediction and translates them to observable and qualitative features such as color, intensity, cell and tissue morphology and texture. We use the Cancer Genome Atlas's Breast Invasive Carcinoma (TCGA-BRCA) histology dataset to build a model predicting patient attributes (disease stage and node status) and the tumor proliferation challenge (TUPAC 2016) breast cancer histology image repository to help identify disease-relevant tissue state (mitotic activity). We find that our enhanced CNN based workflow both increased patient attribute predictive accuracy (~2% increase for disease stage and ~10% increase for node status) and experimentally proved that a data-driven CNN histology model predicting breast invasive carcinoma stages is highly sensitive to features such as color, cell size, and shape, granularity, and uniformity. This work summarizes the need for understanding the widely trusted models built using deep learning and adds a layer of biological context to a technique that functioned as a classification only approach till now. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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