发表机构
University of Information Technology, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam（越南胡志明市信息科技学院; 越南国家大学胡志明市分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨仅用低倍率组织病理学图像实现高效弱监督语义分割，通过模拟低倍率输入并重建，构建框架评估弱监督方法对分辨率退化的响应，发现重建质量指标不能预测分割性能，确定关键退化点，为设计数字病理存储系统提供指导。
AI 中文摘要
全切片图像（WSIs）提供丰富的组织和细胞层面信息，但存储和传输高倍率病理数据资源消耗大，像素级注释也费力耗时。因此研究有限注释（图像级而非像素级标签）的低倍率病理图像能否达到与高倍率图像相当的性能很重要。本文针对不同低分辨率存储设置下的弱监督组织病理学图像分割进行了系统基准研究。从高分辨率图像块开始，模拟低倍率输入并通过插值和基于深度学习的重建方法将其重建回原始大小，再应用弱监督分割管道。该框架能定量评估弱监督方法对不同分辨率退化水平的响应。实验结果表明仅重建质量指标不足以预测下游分割性能，尤其确定了一个关键退化点，此时小尺度结构的定位显著下降。这些发现为设计高效数字病理存储系统并保持可靠自动分析提供了实用指导。代码可在该https网址获取。
英文摘要
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
CommentsAccepted at MAPR 2026
Journal ref2026 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 334-339, 2026
DOI：10.1109/MAPR72750.2026.11685691
