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Ovarian cancer is the most lethal cancer of the female reproductive organs.There are $5$ major histological subtypes of epithelial ovarian cancer, eachwith distinct morphological, genetic, and clinical features. Currently, thesehistotypes are determined by a pathologist's microscopic examination of tumorwhole-slide images (WSI). This process has been hampered by poor inter-observeragreement (Cohen's kappa $0.54$-$0.67$). We utilized a \textit{two}-stage deeptransfer learning algorithm based on convolutional neural networks (CNN) andprogressive resizing for automatic classification of epithelial ovariancarcinoma 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 withoutgynecology-specific training.

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