基于原型的多示例学习用于千兆像素全切片图像分类
Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
AI总结：
针对现有MIL模型解释性不足且不支持人机交互的问题，提出ProtoMIL模型，通过稀疏自编码器提取可解释概念并将预测表示为概念线性组合，在保持分类性能的同时实现透明决策与人工干预。
摘要：
多示例学习（MIL）方法在组织病理学全切片图像（WSI）分析中取得了显著成功。然而，大多数MIL模型仅提供基于注意力的解释，这些解释未能忠实地捕捉模型的决策机制，且不支持人机交互。为了解决这些局限性，我们引入了ProtoMIL，这是一种用于WSI分析的内在可解释MIL模型，提供用户友好的解释并支持人工干预。我们的方法采用稀疏自编码器从图像特征空间中发现人类可解释的概念，随后用这些概念训练ProtoMIL。该模型将预测表示为概念的线性组合，使决策过程透明化。此外，ProtoMIL允许用户通过更改输入概念来进行模型干预。在两个广泛使用的病理学数据集上的实验表明，ProtoMIL的分类性能与最先进的MIL模型相当，同时提供直观易懂的解释。此外，我们证明了该方法能通过人工干预消除对诊断无关信息的依赖，引导模型“因正确理由得出正确结论”。代码将公开于https://github.com/ss-sun/ProtoMIL。
英文摘要：
Multiple Instance Learning (MIL) methods have succeeded remarkably in histopathology whole slide image (WSI) analysis. However, most MIL models only offer attention-based explanations that do not faithfully capture the model's decision mechanism and do not allow human-model interaction. To address these limitations, we introduce ProtoMIL, an inherently interpretable MIL model for WSI analysis that offers user-friendly explanations and supports human intervention. Our approach employs a sparse autoencoder to discover human-interpretable concepts from the image feature space, which are then used to train ProtoMIL. The model represents predictions as linear combinations of concepts, making the decision process transparent. Furthermore, ProtoMIL allows users to perform model interventions by altering the input concepts. Experiments on two widely used pathology datasets demonstrate that ProtoMIL achieves a classification performance comparable to state-of-the-art MIL models while offering intuitively understandable explanations. Moreover, we demonstrate that our method can eliminate reliance on diagnostically irrelevant information via human intervention, guiding the model toward being right for the right reason. Code will be publicly available at https://github.com/ss-sun/ProtoMIL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
