bibtext: @InProceedings{ Fen_Mining_MICCAI2024,
                   author = { Feng, Siyang and Chen, Jiale and Liu, Zhenbing and Liu, Wentao and Wang, Zimin and Lan, Rushi and Pan, Xipeng },
                   title = { { Mining Gold from the Sand: Weakly Supervised Histological Tissue Segmentation with Activation Relocalization and Mutual Learning } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Class activation maps- (CAMs-) based image-level weakly supervised tissue segmentation has became a popular research topic due to the advantage of its low annotation cost. However, there are still two challenges exist in this task: (1) low-quality pseudo masks generation, and (2) training with noisy label supervision. To address these issues, we propose a novel weakly supervised segmentation framework with Activation Relocalization and Mutual Learning (ARML). First, we integrate an Activation Relocalization Scheme (ARS) into classification phase to more accurately cover the useful areas in initial CAMs. Second, to deal with the inevitably noisy annotations in pseudo masks generated by ARS, we propose a noise-robust mutual learning segmentation model. The model promotes peer networks to capture different characteristics of the outputs, and two noise suppression strategies namely samples weighted voting (SWV) and samples relation mining (SRM) are introduced to excavate the potential credible information from noisy annotations. Extensive experiments on BCSS and LUAD-HistoSeg datasets demonstrate that our proposed ARML exceeds many state-of-the-art weakly supervised semantic segmentation methods, which gives a new insight for tissue segmentation tasks. The code is available at: https://github.com/director87/ARML.
title: Mining Gold from the Sand: Weakly Supervised Histological Tissue Segmentation with Activation Relocalization and Mutual Learning
authors: Feng, Siyang Chen, Jiale Liu, Zhenbing Liu, Wentao Wang, Zimin Lan, Rushi Pan, Xipeng