Unsupervised Histopathological Image Semantic Segmentation with Overlapping Patches Consistency Constraint

Massive requirement for pixel-wise annotations in histopathological image segmentation poses a significant challenge, leading to increasing interest in Unsupervised Semantic Segmentation (USS) as a viable alternative. Pre-trained model-based methods have been widely used in USS, achieving promising segmentation performance. However, these methods are less capable for medical image USS tasks due to their limited ability in encoding task-specific contextual information. In this paper, we propose a context-based Overlapping Patches Consistency Constraint (OPCC), which employs the consistency constraint between the local overlapping region’s similarity and global context similarity, achieving consistent class representation in similar environments. Additionally, we introduce an Inter-Layer Self-Attention Fusion (ILSAF) module that employs a multi-head self-attention mechanism along with Inter-Layer Importance-Weighting to generate context-aware and semantically discriminative pixel representations, improving pixel clustering accuracy. Extensive experiments on two public histopathological image segmentation datasets demonstrate that our approach significantly outperforms state-of-the-art methods by a large margin, with mIoU surpassing previous leading work by 5.74 and 8.38 percentage points on the two datasets, respectively.