Kavli Affiliate: Yi Zhou
| First 5 Authors: Kaiwen Huang, Kaiwen Huang, , ,
| Summary:
Semi-supervised learning has gained considerable popularity in medical image
segmentation tasks due to its capability to reduce reliance on expert-examined
annotations. Several mean-teacher (MT) based semi-supervised methods utilize
consistency regularization to effectively leverage valuable information from
unlabeled data. However, these methods often heavily rely on the student model
and overlook the potential impact of cognitive biases within the model.
Furthermore, some methods employ co-training using pseudo-labels derived from
different inputs, yet generating high-confidence pseudo-labels from perturbed
inputs during training remains a significant challenge. In this paper, we
propose an Uncertainty-aware Cross-training framework for semi-supervised
medical image Segmentation (UC-Seg). Our UC-Seg framework incorporates two
distinct subnets to effectively explore and leverage the correlation between
them, thereby mitigating cognitive biases within the model. Specifically, we
present a Cross-subnet Consistency Preservation (CCP) strategy to enhance
feature representation capability and ensure feature consistency across the two
subnets. This strategy enables each subnet to correct its own biases and learn
shared semantics from both labeled and unlabeled data. Additionally, we propose
an Uncertainty-aware Pseudo-label Generation (UPG) component that leverages
segmentation results and corresponding uncertainty maps from both subnets to
generate high-confidence pseudo-labels. We extensively evaluate the proposed
UC-Seg on various medical image segmentation tasks involving different modality
images, such as MRI, CT, ultrasound, colonoscopy, and so on. The results
demonstrate that our method achieves superior segmentation accuracy and
generalization performance compared to other state-of-the-art semi-supervised
methods. Our code will be released at https://github.com/taozh2017/UCSeg.
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