Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
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.
