  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.
