bibtext: @InProceedings{ Hua_Hard_MICCAI2024,
                   author = { Huang, Wentao and Hu, Xiaoling and Abousamra, Shahira and Prasanna, Prateek and Chen, Chao },
                   title = { { Hard Negative Sample Mining for Whole Slide Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs. State-of-the-art methods use self-supervised patch-wise feature representations for multiple instance learning (MIL). Recently, methods have been proposed to fine-tune the feature representation on the downstream task using pseudo labeling, but mostly focusing on selecting high-quality positive patches. In this paper, we propose to mine hard negative samples during fine-tuning. This allows us to obtain better feature representations and reduce the training cost. Furthermore, we propose a novel patch-wise ranking loss in MIL to better exploit these hard negative samples. Experiments on two public datasets demonstrate the efficacy of these proposed ideas. Our codes are available at https://github.com/winston52/HNM-WSI.
title: Hard Negative Sample Mining for Whole Slide Image Classification
authors: Huang, Wentao Hu, Xiaoling Abousamra, Shahira Prasanna, Prateek Chen, Chao