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Deep learning models for poorly differentiated colorectal adenocarcinoma classification in whole slide images using transfer learning
| Content Provider | bioRxiv |
|---|---|
| Author | Tsuneki, Masayuki Kanavati, Fahdi |
| Copyright Year | 2021 |
| Abstract | ABSTRACT Colorectal poorly differentiated adenocarcinoma (ADC) is known to have a poor prognosis as compared with well to moderately differentiated ADC. The frequency of poorly differentiated ADC is relatively low (usually less than 5% among colorectal carcinomas). Histopathological diagnosis based on endoscopic biopsy specimens is currently the most cost effective method to perform as part of colonoscopic screening in average risk patients, and it is an area that could benefit from AI-based tools to aid pathologists in their clinical workflows. In this study, we trained deep learning models to classify poorly differentiated colorectal ADC from Whole Slide Images (WSIs) using a simply transfer learning method. We evaluated the models on a combination of test sets obtained from five distinct sources, achieving receiver operator curve (ROC) area under the curves (AUCs) in the range of 0.94-0.98. |
| Related Links | https://www.biorxiv.org/content/biorxiv/early/2021/05/31/2021.05.31.446384.full.pdf |
| DOI | 10.1101/2021.05.31.446384 |
| Language | English |
| Publisher | Cold Spring Harbor Laboratory |
| Publisher Date | 2021-05-31 |
| Access Restriction | Open |
| Subject Keyword | Pathology |
| Content Type | Text |
| Resource Type | Preprint |
| Subject | Pathology and Forensic Medicine |
