Multiple Instance Learning (MIL) has garnered widespread attention in the field of Whole Slide Image (WSI) classification as it replaces pixel-level manual annotation with diagnostic reports as labels, significantly reducing labor costs. Recent research has shown that bag-level MIL methods often yield better results because they can consider all patches of the WSI as a whole. However, a drawback of such methods is the incorporation of more redundant patches, leading to interference. To extract patches with high diagnostic value while excluding interfering patches to address this issue, we developed an attention-based feature distillation multi-instance learning (AFD-MIL) approach. This approach proposed the exclusion of redundant patches as a preprocessing operation in weakly supervised learning, directly mitigating interference from extensive noise. It also pioneers the use of attention mechanisms to distill features with high diagnostic value, as opposed to the traditional practice of indiscriminately and forcibly integrating all patches. Additionally, we introduced global loss optimization to finely control the feature distillation module. AFD-MIL is orthogonal to many existing MIL methods, leading to consistent performance improvements. This approach has surpassed the current state-of-the-art method, achieving 91.47% ACC (accuracy) and 94.29% AUC (area under the curve) on the Camelyon16 (Camelyon Challenge 2016, breast cancer), while 93.33% ACC and 98.17% AUC on the TCGA-NSCLC (The Cancer Genome Atlas Program: non-small cell lung cancer). Different feature distillation methods were used for the two datasets, tailored to the specific diseases, thereby improving performance and interpretability.

翻译：多示例学习（MIL）在全切片图像（WSI）分类领域获得了广泛关注，因为它以诊断报告作为标签替代了像素级人工标注，显著降低了人力成本。近期研究表明，包级MIL方法通常能取得更好的结果，因为它们能够将WSI的所有切片视为一个整体进行处理。然而，此类方法的缺点在于引入了更多冗余切片，从而导致干扰。为了提取具有高诊断价值的切片，同时排除干扰性切片以解决此问题，我们开发了一种基于注意力的特征蒸馏多示例学习（AFD-MIL）方法。该方法提出将冗余切片排除作为弱监督学习中的预处理操作，直接缓解了来自大量噪声的干扰。它还开创性地利用注意力机制来蒸馏具有高诊断价值的特征，而非传统上不加区分地强行整合所有切片的做法。此外，我们引入了全局损失优化来精细控制特征蒸馏模块。AFD-MIL与许多现有MIL方法是正交的，从而带来了一致的性能提升。该方法超越了当前最先进的方法，在Camelyon16（Camelyon Challenge 2016，乳腺癌）数据集上取得了91.47%的准确率（ACC）和94.29%的曲线下面积（AUC），而在TCGA-NSCLC（癌症基因组图谱计划：非小细胞肺癌）数据集上则达到了93.33%的ACC和98.17%的AUC。针对两种数据集采用了不同的特征蒸馏方法，以适应特定疾病，从而提升了性能与可解释性。