Hard Negative Sample Mining for Whole Slide Image Classification
<|reference_start|>Hard Negative Sample Mining for Whole Slide Image Classification: 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<|reference_end|>
@article{huang2024hard,
    title={Hard Negative Sample Mining for Whole Slide Image Classification},
    author={Wentao Huang, Xiaoling Hu, Shahira Abousamra, Prateek Prasanna, Chao
  Chen},
    journal={arXiv preprint arXiv:2410.02212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02212},
    primaryClass={cs.CV}
}