The content introduces a new method using Shapley values to enhance the accuracy of instance importance scores in multiple-instance learning for whole-slide image classification. The proposed framework shows superior performance over existing methods on various datasets, offering enhanced interpretability and class-wise insights.
The study addresses challenges in attention-based MIL methods by introducing an accelerated Shapley value computation technique. This approach improves the allocation of pseudo bags and enhances model training diversity. Extensive experiments demonstrate the effectiveness of the proposed method across different datasets.
Key points include the introduction of Shapley values for IIS estimation, progressive pseudo bag augmentation, and the use of EM algorithm for optimal pseudo bag label assignment. The visualization results highlight the effectiveness of Shapley value-based IIS in accurately identifying positive instances.
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by Renao Yan,Qi... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2312.05490.pdfDeeper Inquiries