Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning.

We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed attention accumulation and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research.