Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
This work addresses the problem of high annotation costs for histopathological image segmentation for medical researchers, representing an incremental improvement over existing weakly supervised methods.
The paper tackles incomplete segmentation masks in weakly supervised histopathological image segmentation by proposing a prototype-based image prompting framework that uses clustering and contrastive learning to capture intra-class heterogeneity and address inter-class homogeneity, resulting in outperforming existing methods on four datasets and setting new benchmarks.
Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions, leading to incomplete masks. Recent approaches that introduce textual information struggle with histopathological images due to inter-class homogeneity and intra-class heterogeneity. In this paper, we propose a prototype-based image prompting framework for histopathological image segmentation. It constructs an image bank from the training set using clustering, extracting multiple prototype features per class to capture intra-class heterogeneity. By designing a matching loss between input features and class-specific prototypes using contrastive learning, our method addresses inter-class homogeneity and guides the model to generate more accurate CAMs. Experiments on four datasets (LUAD-HistoSeg, BCSS-WSSS, GCSS, and BCSS) show that our method outperforms existing weakly supervised segmentation approaches, setting new benchmarks in histopathological image segmentation.