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Computerized diagnosis of prostate cancer based on whole slide histology images and deep learning methods
(2021) Malyshev, V.; Voynov, D.; Lapo, E.
This paper addresses the problem of the diagnosis of prostate cancer and providing the final score in the commonly used ISUP scale. The input data consisted of 10 616 whole-slide histological images with the size of the largest dimension up to 100 000 pixels and 22 089 of their image tiles of 256x256 pixels in size. Several approaches were examined including conventional methods based on feature extraction followed by suitable classifiers as well as various deep learning techniques based on convolutional neural networks of different architectures. During the experiments, two deep learning approaches have been shown to be the most efficient. The first one implements a technique of sequential, tile-by-tile image classification using ResNet50 as a feature extractor followed by a prediction of final ISUP score based on the informative features using a small convolutional network. The second approach predicts the final ISUP score using an ensemble of 4 deep learning models for regression from a subset of the most prominent image tiles first, combined into one single pseudo-image. Being independently tested on an unknown image dataset that was not available for authors, these approaches achieved the prediction accuracy of 0.8110 and 0.9277 respectively.