An end-to-end weakly supervised learning framework for cancer subtype classification using histopathological slides

AI-powered analysis of histopathology data has become an invaluable assistant for pathologists due to its efficiency and accuracy. However, existing deep learning methods still face some challenges in specifying cancer subtypes. For example, the ultra-high resolution of histopathological slides generally contains numerous redundant features, which are not useful for cancer subtype classification and thus lead to considerable computational costs. Moreover, the lack of manual annotations of disease-specific regions (i.e., patch-level annotations) from experts makes it more difficult to learn such histological features with only slide-level labels. In this paper, we propose an end-to-end weakly supervised learning framework called EWSLF to address these issues. First, we employ a cluster-based sampling strategy to refine the histological features for further training, which can improve classification accuracy and reduce computational cost. Second, we employ a multi-branch attention mechanism to produce patch-level pseudo-labels and aggregate the patch features into slide-level features, which can complement the missing patch-level labels from experts. Experimental results on both public and in-house datasets demonstrate the superiority and credible results of our model compared with the state-of-the-art methods for cancer subtype classification. Code: https://github.com/hongren21/ewslf.

Automated summary

This paper proposes an end-to-end weakly supervised learning framework called EWSLF to address challenges in histopathology data analysis. The framework utilizes cluster-based sampling and multi-branch attention mechanism to refine histological features and improve classification accuracy for cancer subtype classification. Experimental results demonstrate the superior and credible results of the proposed model compared to state-of-the-art methods.