用病理先验缓解乳腺癌病理切片分类中的遗忘问题
Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification
- 引入微观与宏观病理先验，优化候选图像块选择
- 多任务分类头结合缩略图提示，提升新旧任务平衡性
- 在多个公开数据集上优于现有持续学习方法
组织病理学中，智能诊断全切片图像（WSI）对实现诊断自动化和客观化至关重要，可减轻病理科医生的工作负担。然而，模型在不同来源数据集上的增量训练常面临遗忘已学知识的问题。为此，我们提出一种新框架PaGMIL，用于缓解乳腺癌WSI分类中的灾难性遗忘。该框架在通用的多实例学习（MIL）架构中引入两个关键组件：首先，利用显微病理先验选择更准确、多样化的代表性图像块；其次，为每个任务训练独立分类头，并利用宏观病理先验知识，将缩略图作为提示引导（PG），动态选择合适分类头。我们在多个公开乳腺癌数据集上评估了PaGMIL的持续学习性能，结果表明其在当前任务表现与旧任务保留之间取得了更好平衡，显著优于其他持续学习方法。代码将在论文接受后开源。
原文摘要 · Abstract (English)
In histopathology, intelligent diagnosis of Whole Slide Images (WSIs) is essential for automating and objectifying diagnoses, reducing the workload of pathologists. However, diagnostic models often face the challenge of forgetting previously learned data during incremental training on datasets from different sources. To address this issue, we propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification. Our framework introduces two key components into the common MIL model architecture. First, it leverages microscopic pathological prior to select more accurate and diverse representative patches for MIL. Secondly, it trains separate classification heads for each task and uses macroscopic pathological prior knowledge, treating the thumbnail as a prompt guide (PG) to select the appropriate classification head. We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets. PaGMIL achieves a better balance between the performance of the current task and the retention of previous tasks, outperforming other continual learning methods. Our code will be open-sourced upon acceptance.
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