Abstract
Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets. The field of WSI analysis is confronted with a severe long-tailed distribution problem, which significantly impacts the performance of classifiers. Long-tailed distributions lead to class imbalance, where some classes have sparse samples while others are abundant, making it difficult for classifiers to accurately identify minority class samples. To address this issue, we propose an ensemble learning method based on MIL, which employs expert decoders with shared aggregators and consistency constraints to learn diverse distributions and reduce the impact of class imbalance on classifier performance. Moreover, we introduce a multimodal distillation framework that leverages text encoders pre-trained on pathology-text pairs to distill knowledge and guide the MIL aggregator in capturing stronger semantic features relevant to class information. To ensure flexibility, we use learnable prompts to guide the distillation process of the pre-trained text encoder, avoiding limitations imposed by specific prompts. Our method, MDE-MIL, integrates multiple expert branches focusing on specific data distributions to address long-tailed issues. Consistency control ensures generalization across classes. Multimodal distillation enhances feature extraction. Experiments on Camelyon+-LT and PANDA-LT datasets show it outperforms state-of-the-art methods.
Abstract (translated)
多重实例学习（MIL）在计算病理学中扮演着重要角色，它能够对整个滑动图像（WSI）数据集进行弱监督分析。然而，WSI 分析领域面临着严重的长尾分布问题，这对分类器的性能产生了显著影响。长尾分布导致类别不平衡的问题，在这个问题中，某些类别的样本非常稀少而另一些类别则异常丰富，这使得分类器难以准确地识别少数类样本。为了解决这一问题，我们提出了一种基于MIL的集成学习方法，该方法使用共享聚合器和一致性的专家解码器来学习多样的分布，并减少类别不平衡对分类器性能的影响。 此外，我们还引入了一个多模态知识蒸馏框架，利用预先在病理学图像-文本对上训练的文本编码器来提取并指导MIL聚合器捕获与类信息相关的更强大的语义特征。为了确保灵活性，我们在预训练的文本编码器的知识蒸馏过程中使用了可学习提示，以避免特定提示的限制。 我们的方法 MDE-MIL 集成了多个专注于特定数据分布的专家分支来解决长尾问题。一致性控制保证了跨类别的泛化能力，而多模态知识蒸馏则增强了特征提取能力。实验结果表明，在Camelyon+-LT和PANDA-LT数据集上的测试中，我们的方法超过了现有的最先进的方法。
URL
https://arxiv.org/abs/2503.00915
