Continual Learning for Weakly-Supervised Histopathology Tissue Segmentation

Weakly supervised histopathology segmentation is a widely studied field that aims to achieve pixel-level semantic segmentation using image-level annotations, reducing the need for labor-intensive labeling. Despite significant advances in this task, existing methods assume the availability of all training data at once during training. Since medical image collections typically expand over time in practice, such methods become impractical. Meanwhile, research on continual semantic segmentation has also made significant progress. However, most existing works still rely on pixel-level annotations to train models. As a result, integrating continual learning into weakly supervised segmentation models has emerged as a promising direction. To address this challenge, we propose CL4WSeg, a novel end-to-end transformer-based framework that employs temporal distillation to leverage features from previous models for continual weakly supervised segmentation. Furthermore, we utilize a controllable diffusion model to enable generative replay and integrate an image quality filter to collect high-quality images, alleviating catastrophic forgetting. Experiments on the LUAD-HistoSeg, BCSS-WSSS, and WSSS4LUAD datasets demonstrate that our approach outperforms state-of-the-art methods.