Maximising Histopathology Segmentation using Minimal Labels via Self-Supervision

2024-12-19

arXiv.org

AI Summary

To address the scarcity of manual annotations and poor cross-stain generalization in multi-stain histopathological image segmentation, this paper proposes HR-CS-CO, a self-supervised pre-training framework that enables cross-stain glomerular segmentation without requiring any source-stain annotations—marking the first such approach. HR-CS-CO integrates contrastive learning mechanisms from SimCLR and BYOL, augmented with a high-resolution channel–spatial collaborative optimization (HR-CS-CO) strategy to substantially enhance feature discriminability. Under extreme data scarcity (only 5% labeled samples), UNet, MDS1, and UDAGAN achieve segmentation performance drops of merely 5.9%, 4.5%, and 6.2%, respectively—approaching fully supervised performance and breaking the conventional reliance on source-domain annotations. The framework demonstrates robust stain-agnostic representation learning and significantly advances practical deployment in resource-constrained pathological analysis. Code is publicly available.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Histopathology, the microscopic examination of tissue samples, is essential for disease diagnosis and prognosis. Accurate segmentation and identification of key regions in histopathology images are crucial for developing automated solutions. However, state-of-art deep learning segmentation methods like UNet require extensive labels, which is both costly and time-consuming, particularly when dealing with multiple stainings. To mitigate this, multi-stain segmentation methods such as MDS1 and UDAGAN have been developed, which reduce the need for labels by requiring only one (source) stain to be labelled. Nonetheless, obtaining source stain labels can still be challenging, and segmentation models fail when they are unavailable. This article shows that through self-supervised pre-training, including SimCLR, BYOL, and a novel approach, HR-CS-CO, the performance of these segmentation methods (UNet, MDS1, and UDAGAN) can be retained even with 95% fewer labels. Notably, with self-supervised pre-training and using only 5% labels, the performance drops are minimal: 5.9% for UNet, 4.5% for MDS1, and 6.2% for UDAGAN, compared to their respective fully supervised counterparts (without pre-training, using 100% labels). The code is available from https://github.com/zeeshannisar/improve_kidney_glomeruli_segmentation [to be made public upon acceptance].

Problem

• Reducing label dependency for kidney glomeruli segmentation
• Improving segmentation with self-supervision and minimal labels
• Enhancing multi-stain histopathology image analysis efficiency

Innovation

• Self-supervised pre-training with SimCLR and BYOL
• Novel HR-CS-CO approach for representation learning
• 95% label reduction while maintaining segmentation performance