# Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images

Source: [Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images](<https://www.sota2.com/research/paper/68fa76791e209131cea6d3ee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:55:27.100Z

Authors: Hongyi Wu, Hong Zhang

Published: 2025-01-07T15:54:03.000Z

## Abstract

With the rapid advancement of deep learning, computational pathology has made significant progress in cancer diagnosis and subtyping. Tissue segmentation is a core challenge, essential for prognosis and treatment decisions. Weakly supervised semantic segmentation (WSSS) reduces the annotation requirement by using image-level labels instead of pixel-level ones. However, Class Activation Map (CAM)-based methods still suffer from low spatial resolution and unclear boundaries. To address these issues, we propose a multi-level superpixel correction algorithm that refines CAM boundaries using superpixel clustering and floodfill. Experimental results show that our method achieves great performance on breast cancer segmentation dataset with mIoU of 71.08%, significantly improving tumor microenvironment boundary delineation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.03891>)

[PDF](<https://arxiv.org/pdf/2501.03891.pdf>)