Summary of Hisynseg: Weakly-supervised Histopathological Image Segmentation Via Image-mixing Synthesis and Consistency Regularization, by Zijie Fang et al.

HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization

by Zijie Fang, Yifeng Wang, Peizhang Xie, Zhi Wang, Yongbing Zhang

First submitted to arxiv on: 30 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper proposes a novel weakly-supervised semantic segmentation framework for histopathological images called HisynSeg, which addresses the limitations of class activation map (CAM)-based methods in tissue semantic segmentation. The framework combines image-mixing synthesis and consistency regularization to generate synthesized images with pixel-level masks for fully-supervised model training. Additionally, an image filtering module ensures the authenticity of synthesized images, while a self-supervised consistency regularization prevents overfitting to occasional synthesis artifacts. Experimental results on three datasets demonstrate that HisynSeg achieves state-of-the-art performance.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper helps computers better understand and segment tissues in medical images without needing lots of detailed labels. It develops a new way to do this called HisynSeg, which uses fake image generation and checks to make sure the results are correct. This method is better than others that try to use class activation maps, which can sometimes produce poor results. The new approach improves accuracy by generating realistic images and checking its own work.