Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Multiple instance learning (MIL) significantly reduced annotation costs via
bag-level weak labels for large-scale images, such as histopathological whole
slide images (WSIs). However, its adaptability to continual tasks with minimal
forgetting has been rarely explored, especially on instance classification for
localization. Weakly incremental learning for semantic segmentation has been
studied for continual localization, but it focused on natural images,
leveraging global relationships among hundreds of small patches (e.g., $16
\times 16$) using pre-trained models. This approach seems infeasible for MIL
localization due to enormous amounts ($\sim 10^5$) of large patches (e.g., $256
\times 256$) and no available global relationships such as cancer cells. To
address these challenges, we propose Continual Multiple Instance Learning with
Enhanced Localization (CoMEL), an MIL framework for both localization and
adaptability with minimal forgetting. CoMEL consists of (1) Grouped Double
Attention Transformer (GDAT) for efficient instance encoding, (2) Bag
Prototypes-based Pseudo-Labeling (BPPL) for reliable instance pseudo-labeling,
and (3) Orthogonal Weighted Low-Rank Adaptation (OWLoRA) to mitigate forgetting
in both bag and instance classification. Extensive experiments on three public
WSI datasets demonstrate superior performance of CoMEL, outperforming the prior
arts by up to $11.00\%$ in bag-level accuracy and up to $23.4\%$ in
localization accuracy under the continual MIL setup.
