病理先验引导的多实例学习以缓解乳腺癌全切片图像分类中的灾难性遗忘
Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification
AI总结：
提出PaGMIL框架，利用微观和宏观病理先验增强多实例学习，通过选择代表性斑块和提示引导分类头，缓解乳腺癌全切片图像分类中的灾难性遗忘，并在多个公开数据集上优于现有持续学习方法。
摘要：
在组织病理学中，全切片图像（WSIs）的智能诊断对于实现诊断的自动化和客观化、减轻病理学家的工作负担至关重要。然而，在来自不同来源的数据集上进行增量训练时，诊断模型常常面临遗忘先前学习数据的挑战。为了解决这一问题，我们提出了一个新框架PaGMIL，以缓解乳腺癌WSI分类中的灾难性遗忘。我们的框架在常见的MIL模型架构中引入了两个关键组件。首先，它利用微观病理先验来选择更准确和多样化的代表性斑块用于MIL。其次，它为每个任务训练独立的分类头，并利用宏观病理先验知识，将缩略图视为提示引导（PG）以选择适当的分类头。我们在多个公开的乳腺癌数据集上评估了PaGMIL的持续学习性能。PaGMIL在当前任务的性能和先前任务的保留之间取得了更好的平衡，优于其他持续学习方法。我们的代码将在接受后开源。
英文摘要：
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.
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。
