Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning

Yiping Wang, David Farnell, Hossein Farahani, Mitchell Nursey, Basile Tessier-Cloutier, Steven J.M. Jones, David G. Huntsman, C. Blake Gilks, Ali Bashashati

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 two-stage 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.

Paper Type: methodological development

Track: short paper

Keywords: Transfer learning, Ovarian cancer, Digital pathology

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