Mind the Context: Attention-Guided Weak-to-Strong Consistency for Enhanced Semi-Supervised Medical Image Segmentation

TL;DR

This paper introduces AIGCMatch, a semi-supervised learning framework that uses attention-guided perturbations to improve medical image segmentation by leveraging unlabeled data, achieving state-of-the-art results on key datasets.

Contribution

It proposes a novel attention-guided perturbation strategy for semi-supervised medical image segmentation, enhancing structural preservation and semantic understanding.

Findings

Achieved 90.4% Dice score on ACDC dataset.

Outperformed existing semi-supervised methods.

Validated effectiveness on multiple medical imaging datasets.

Abstract

Medical image segmentation is a pivotal step in diagnostic and therapeutic processes, relying on high-quality annotated data that is often challenging and costly to obtain. Semi-supervised learning offers a promising approach to enhance model performance by leveraging unlabeled data. Although weak-to-strong consistency is a prevalent method in semi-supervised image segmentation, there is a scarcity of research on perturbation strategies specifically tailored for semi-supervised medical image segmentation tasks. To address this challenge, this paper introduces a simple yet efficient semi-supervised learning framework named Attention-Guided weak-to-strong Consistency Match (AIGCMatch). The AIGCMatch framework incorporates attention-guided perturbation strategies at both the image and feature levels to achieve weak-to-strong consistency regularization. This method not only preserves the structural information of medical images but also enhances the model's ability to process complex semantic information. Extensive experiments conducted on the ACDC and ISIC-2017 datasets have validated the effectiveness of AIGCMatch. Our method achieved a 90.4\% Dice score in the 7-case scenario on the ACDC dataset, surpassing the state-of-the-art methods and demonstrating its potential and efficacy in clinical settings.