  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 \textit{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.

Image and Video Processing;Computer Vision and Pattern Recognition