M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis
<|reference_start|>M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis: Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers. However, existing MIL methods predominantly focus on single-task learning, resulting in not only overall low efficiency but also the overlook of inter-task relatedness. To address these issues, we proposed an adapted architecture of Multi-gate Mixture-of-experts with Multi-proxy for Multiple instance learning (M4), and applied this framework for simultaneous prediction of multiple genetic mutations from WSIs. The proposed M4 model has two main innovations: (1) utilizing a mixture of experts with multiple gating strategies for multi-genetic mutation prediction on a single pathological slide; (2) constructing multi-proxy expert network and gate network for comprehensive and effective modeling of pathological image information. Our model achieved significant improvements across five tested TCGA datasets in comparison to current state-of-the-art single-task methods. The code is available at:https://github.com/Bigyehahaha/M4.<|reference_end|>
@article{li2024m4:,
    title={M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple
  Instance Learning in Histopathology Image Analysis},
    author={Junyu Li and Ye Zhang and Wen Shu and Xiaobing Feng and Yingchun Wang
  and Pengju Yan and Xiaolin Li and Chulin Sha and Min He},
    journal={arXiv preprint arXiv:2407.17267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17267},
    primaryClass={cs.CV}
}