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Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wang, Yiping Farnell, David A. Farahani, Hossein Nursey, Mitchell Tessier-Cloutier, Basile Jones, Steven J. M. Huntsman, David G. Gilks, Cyril Blake Bashashati, Ali |
| Copyright Year | 2020 |
| Abstract | Ovarian cancer is the most lethal cancer of the female reproductive organs. There are 5 major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been hampered by poor inter-observer agreement (Cohen's kappa 0.54-0.67). We utilized a twostage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of epithelial ovarian carcinoma WSIs. The proposed algorithm achieved a mean accuracy of 87.54% and Cohen's kappa of 0.8106 in the slide-level classification of 305 WSIs; performing better than a standard CNN and pathologists without gynecology-specific training. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://openreview.net/pdf?id=VXdQD8B307 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
