Efficient Self-Supervised Grading of Prostate Cancer Pathology

Abstract

Prostate cancer grading, using the International Society of Urological Pathology (ISUP) system, for treatment decisions is highly subjective and requires considerable expertise. Despite advances in computer-aided diagnosis systems, few have handled efficient ISUP grading on whole slide images (WSIs) of prostate biopsies based only on slide labels. In this scenario, TSOR is developed, where a novel task-specific self-supervised learning (SSL) framework is used for patch-level pretraining. This is fine-tuned using ordinal regression for WSI-level ISUP grading. One of the main challenges faced by deep learning (DL) in ISUP grading, is the learning of patch-level features based on slide labels. Though using models pretrained at patch-level using SSL or other paradigms is the most obvious choice here, the diversity of training samples plays a crucial role in effective pretraining. However, pretraining on a large database of different histopathology images becomes computationally expensive. Therefore, a patch-level dataset (relatively balanced with respect to the patch-level grades) is initially created for effective SSL-based pretraining. As stain-variation across centers leads to difficulty in generalization, an additional loss term is incorporated to effectively learn the stain-agnostic patch-level features. As it is desirable that misclassification be as close as possible to the actual grade, in medical images, we fine-tune the pretrained network for WSI-level ISUP grading using an ordinal regression-based approach. Experimental results on the most extensive prostate cancer grade assessment (PANDA) challenge dataset, and the SICAPv2 dataset, demonstrate the effectiveness of TSOR compared to state-of-the-art (SOTA) methods.

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