CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio

TL;DR

CAMEL2 improves weakly supervised histopathology image segmentation by incorporating a significance ratio threshold, enabling larger image inputs and achieving performance comparable to fully supervised methods with minimal labeling effort.

Contribution

CAMEL2 introduces a significance ratio threshold to enhance weakly supervised learning, allowing larger image inputs and maintaining high segmentation accuracy.

Findings

CAMEL2 achieves comparable results to fully supervised methods.

It scales up image input size from 1,280x1,280 to 5,120x5,120.

Uses easy-to-annotate binary labels for large images.

Abstract

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, we proposed CAMEL, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1,280x1,280 image-level binary annotations for positive WSIs. Here, we present CAMEL2, by introducing a threshold of the cancerous ratio for positive bags, it allows us to better utilize the information, consequently enabling us to scale up the image-level setting from 1,280x1,280 to 5,120x5,120 while maintaining the accuracy. Our results with various datasets, demonstrate that CAMEL2, with the help of 5,120x5,120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications.