Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
This work proposes a novel framework, Uncertainty-Guided Cross Attention
Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in
semi-supervised medical image segmentation. UG-CEMT leverages the strengths of
co-training and knowledge distillation by combining a Cross-attention Ensemble
Mean Teacher framework (CEMT) inspired by Vision Transformers (ViT) with
uncertainty-guided consistency regularization and Sharpness-Aware Minimization
emphasizing uncertainty. UG-CEMT improves semi-supervised performance while
maintaining a consistent network architecture and task setting by fostering
high disparity between sub-networks. Experiments demonstrate significant
advantages over existing methods like Mean Teacher and Cross-pseudo Supervision
in terms of disparity, domain generalization, and medical image segmentation
performance. UG-CEMT achieves state-of-the-art results on multi-center prostate
MRI and cardiac MRI datasets, where object segmentation is particularly
challenging. Our results show that using only 10\% labeled data, UG-CEMT
approaches the performance of fully supervised methods, demonstrating its
effectiveness in exploiting unlabeled data for robust medical image
segmentation. The code is publicly available at
\url{https://github.com/Meghnak13/UG-CEMT}
