PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation

TL;DR

This paper introduces PMT, a semi-supervised learning framework for medical image segmentation that enhances pseudo-label quality through co-training of diverse models and a difference-driven regularizer, improving performance on CT and MRI datasets.

Contribution

The proposed PMT framework employs dual mean teacher models with maintained diversity and a difference-driven regularizer to improve pseudo-label quality in semi-supervised medical image segmentation.

Findings

Outperforms state-of-the-art methods on CT and MRI datasets.

Effectively maintains model diversity for better pseudo-labels.

Enhances segmentation accuracy with a simple pseudo-label filtering strategy.

Abstract

Semi-supervised learning has emerged as a widely adopted technique in the field of medical image segmentation. The existing works either focuses on the construction of consistency constraints or the generation of pseudo labels to provide high-quality supervisory signals, whose main challenge mainly comes from how to keep the continuous improvement of model capabilities. In this paper, we propose a simple yet effective semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation, whose goal is to generate high-fidelity pseudo labels by learning robust and diverse features in the training process. Specifically, our PMT employs a standard mean teacher to penalize the consistency of the current state and utilizes two sets of MT architectures for co-training. The two sets of MT architectures are individually updated for prolonged periods to maintain stable model diversity established through performance gaps generated by iteration differences. Additionally, a difference-driven alignment regularizer is employed to expedite the alignment of lagging models with the representation capabilities of leading models. Furthermore, a simple yet effective pseudo-label filtering algorithm is employed for facile evaluation of models and selection of high-fidelity pseudo-labels outputted when models are operating at high performance for co-training purposes. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches across various dimensions. The code is available at https://github.com/Axi404/PMT.