MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features

TL;DR

This paper introduces a scalable weakly supervised method for ovarian cancer subtype classification and localisation that leverages contrastive and prototype learning with frozen patch features, significantly improving accuracy and efficiency.

Contribution

It presents a novel approach combining contrastive and prototype learning with frozen features, enhancing scalability and accuracy over traditional methods.

Findings

70.4% improvement in F1 score for instance classification

15.3% improvement in F1 score for slide classification

Significant gains in AUC for localisation and classification

Abstract

The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches. While traditional approaches in this field have relied on pre-computed, frozen image features, recent advances have shifted towards end-to-end feature extraction, providing an improvement in accuracy but at the expense of significantly reduced scalability during training and time-consuming experimentation. In this paper, we propose a new approach for subtype classification and localisation in ovarian cancer histopathology images using contrastive and prototype learning with pre-computed, frozen features via feature-space augmentations. Compared to DSMIL, our method achieves an improvement of 70.4\% and 15.3\% in F1 score for instance- and slide-level classification, respectively, along with AUC gains of 16.9\% for instance localisation and 2.3\% for slide classification, while maintaining the use of frozen patch features.