Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

TL;DR

This paper introduces OEEM, a novel weakly-supervised gland segmentation method from histology images that outperforms many fully and weakly-supervised approaches by focusing on credible supervision signals.

Contribution

The paper proposes OEEM, a new online easy example mining technique tailored for gland segmentation, addressing challenges of class confusion and noisy pseudo-labels.

Findings

OEEM surpasses fully-supervised methods in gland segmentation accuracy.

OEEM outperforms existing weakly-supervised methods by over 6% in mIoU.

The approach effectively mitigates false predictions in pseudo-masks.

Abstract

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis; however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among different tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, therefore mitigating the influence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.4% and 6.04% at mIoU, respectively. Code is available at https://github.com/xmed-lab/OEEM.