Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
| Autor: | Christian Haudenschild, Louis J. Vaickus, Joshua J. Levy, Clark Barwick, Brock C. Christensen |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
medicine.diagnostic_test Pixel business.industry Computer science Deep learning Message passing Dimension (graph theory) Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Digital pathology Histology Topology Visualization 03 medical and health sciences 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis Biopsy medicine Topological data analysis Artificial intelligence business |
| Zdroj: | PSB |
| DOI: | 10.1142/9789811232701_0027 |
| Popis: | Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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