AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification
<|reference_start|>AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification: Multiple Instance Learning (MIL) has demonstrated effectiveness in analyzing whole slide images (WSIs), yet it often encounters overfitting challenges in real-world applications, particularly in the form of attention over-concentration. While existing methods to alleviate this issue introduce complex modules or processing steps, such as multiple-stage training and teacher-student distillation, this paper proposes a simple yet effective regularization: Attention Entropy Maximization (AEM). Motivated by our investigation revealing a positive correlation between attention entropy and model performance, AEM incorporates a negative entropy loss for attention values into the standard MIL framework, penalizing overly concentrated attention and encouraging the model to consider a broader range of informative regions in WSIs, potentially improving its generalization capabilities. Compared to existing overfitting mitigation methods, our AEM approach offers advantages of simplicity, efficiency, and versatility. It requires no additional modules or processing steps, involves only one hyperparameter, and demonstrates compatibility with MIL frameworks and techniques. These advantages make AEM particularly attractive for practical applications. We evaluate AEM on three benchmark datasets, demonstrating consistent performance improvements over existing methods. Furthermore, AEM shows high versatility, integrating effectively with four feature extractors, two advanced MIL frameworks, three attention mechanisms, and Subsampling augmentation technique. The source code is available at \url{https://github.com/dazhangyu123/AEM}.<|reference_end|>
@article{zhang2024aem:,
    title={AEM: Attention Entropy Maximization for Multiple Instance Learning based
  Whole Slide Image Classification},
    author={Yunlong Zhang and Zhongyi Shui and Yunxuan Sun and Honglin Li and
  Jingxiong Li and Chenglu Zhu and Lin Yang},
    journal={arXiv preprint arXiv:2406.15303},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15303},
    primaryClass={cs.CV}
}