  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.
