Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis

TL;DR

This paper introduces AGLR-CL, an attention-based generative continual learning framework for WSI classification that effectively handles domain shifts without storing original data, ensuring privacy and high performance across diverse datasets.

Contribution

The paper presents a novel attention-based generative replay method using GMMs for domain-incremental WSI classification, eliminating the need for replay buffers and preserving privacy.

Findings

Outperforms buffer-free methods in domain adaptation

Matches performance of buffer-based continual learning approaches

Effective across diverse datasets and clinical tasks

Abstract

Whole slide image (WSI) classification has emerged as a powerful tool in computational pathology, but remains constrained by domain shifts, e.g., due to different organs, diseases, or institution-specific variations. To address this challenge, we propose an Attention-based Generative Latent Replay Continual Learning framework (AGLR-CL), in a multiple instance learning (MIL) setup for domain incremental WSI classification. Our method employs Gaussian Mixture Models (GMMs) to synthesize WSI representations and patch count distributions, preserving knowledge of past domains without explicitly storing original data. A novel attention-based filtering step focuses on the most salient patch embeddings, ensuring high-quality synthetic samples. This privacy-aware strategy obviates the need for replay buffers and outperforms other buffer-free counterparts while matching the performance of buffer-based solutions. We validate AGLR-CL on clinically relevant biomarker detection and molecular status prediction across multiple public datasets with diverse centers, organs, and patient cohorts. Experimental results confirm its ability to retain prior knowledge and adapt to new domains, offering an effective, privacy-preserving avenue for domain incremental continual learning in WSI classification.