Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images

2026-07-12

AI Summary

This study addresses the high storage and annotation costs of high-magnification histopathology images, which hinder the widespread adoption of semantic segmentation. It systematically evaluates the feasibility of weakly supervised semantic segmentation using only low-magnification images and image-level labels. The authors reconstruct low-resolution images to their original size via interpolation and deep learning, integrating them into a weakly supervised segmentation pipeline within an end-to-end evaluation framework. Their work reveals, for the first time, that conventional image reconstruction quality metrics poorly predict downstream segmentation performance and identifies a critical resolution threshold below which the ability to localize fine-scale structures deteriorates significantly. Experimental results demonstrate a sharp decline in segmentation accuracy beneath this threshold, elucidating both the potential and limitations of low-magnification images in weakly supervised settings and offering practical guidance for efficient storage and automated analysis in digital pathology systems.

Abstract

Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive. Moreover, annotating WSIs at the pixel level is labor-intensive and time-consuming. Therefore, it is important to investigate whether low-magnification pathology images with limited annotations (i.e., image-level instead of pixel-level labels) can achieve performance comparable to high-magnification images. This paper presents a systematic benchmark study on weakly supervised histopathological image segmentation under different low-resolution storage settings. Starting from high-resolution image patches, we simulate lower-magnification inputs and reconstruct them to the original size using interpolation and deep learning-based reconstruction methods before applying the weakly-supervised segmentation pipeline. This framework enables a quantitative evaluation of how weakly supervised methods respond to different levels of resolution degradation. Experimental results show that reconstruction quality metrics alone are insufficient to predict downstream segmentation performance. In particular, the study identifies a critical degradation point where the localization of small-scale structures declines significantly. These findings provide practical guidance for designing efficient digital pathology storage systems while maintaining reliable automated analysis. Code is available at https://github.com/Dung-Dx/LowMagWSS

Problem

• weakly supervised semantic segmentation
• low-magnification histopathological images
• whole-slide images
• resolution degradation
• digital pathology storage

Innovation

• weakly supervised learning
• low-magnification histopathology
• semantic segmentation
• resolution degradation
• image reconstruction