Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
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.
