AI 中文总结
DeCo-MIL通过频率去偏反事实推理，缓解全幻灯片图像分析的嵌套双长尾问题，在三个长尾WSI基准上实现了尾部类别识别与整体分类的最先进性能。
AI 中文摘要
多实例学习（MIL）被广泛应用于弱监督全幻灯片图像（WSI）分析。然而在长尾分布下，基于MIL的WSI分析面临嵌套双长尾：幻灯片间的类别长尾，以及实例级判别证据的幻灯片内长尾。这两个长尾相互耦合：尾部类别训练幻灯片极少，且其有限的诊断证据集中在少数图像块中，被包内大量冗余所掩盖。这种耦合会使模型偏向头部类别，降低稀有类别的识别性能。为解决该问题，我们提出用于长尾WSI分析的DeCo-MIL，通过频率去偏反事实推理共同缓解嵌套双长尾。针对内部长尾，DeCo-MIL将图像块聚类为组织形态锚点，用匹配的正常原型替换每个锚点以执行反事实干预，并使用类别频率校正后的预测估计其对真实类别的反事实贡献。这些贡献指导冗余掩码以保留稀缺的判别实例。针对外部长尾，DeCo-MIL从冗余减少的包中构建锚点分层伪包，结合尾部感知过采样与一致性正则化，在保留组织形态构成的同时，增加尾部类别的有效监督。在三个长尾WSI基准上的大量实验表明，DeCo-MIL在尾部类别识别和整体分类中均达到了最先进的性能。
英文摘要
Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nested dual long-tail: an inter-slide class long tail and an intra-slide long tail of instance-level discriminative evidence. The two long tails are coupled: tail classes have few training slides, while their limited diagnostic evidence is concentrated in a few patches and obscured by abundant within-bag redundancy. This coupling biases models toward head classes and degrades rare-class recognition. To address this, we propose DeCo-MIL for long-tailed WSI analysis, which jointly alleviates the nested dual long-tail through frequency-debiased counterfactual reasoning. For the inner long tail, DeCo-MIL clusters patches into tissue-morphology anchors, replaces each anchor with its matched normal prototype to perform a counterfactual intervention, and estimates its counterfactual contribution to the ground-truth class using class-frequency-corrected predictions. These contributions guide redundancy masking to preserve scarce discriminative instances. For the outer long tail, DeCo-MIL constructs anchor-stratified pseudo-bags from redundancy-reduced bags and combines tail-aware oversampling with consistency regularization, increasing effective supervision for tail classes while preserving tissue-morphology composition. Extensive experiments on three long-tailed WSI benchmarks demonstrate that DeCo-MIL achieves state-of-the-art performance in both tail-class recognition and overall classification.
