AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation

TL;DR

This paper introduces AdvMIM, an adversarial masked image modeling approach that enhances semi-supervised medical image segmentation by increasing supervision signals and reducing domain gaps between original and masked data, significantly improving performance.

Contribution

The paper proposes a novel adversarial masked image modeling method that leverages masked domain training and adversarial loss to improve transformer-based semi-supervised segmentation, extending to CNNs.

Findings

Outperforms existing semi-supervised methods on three datasets

Effectively increases supervision signals via masked image modeling

Reduces domain gap with adversarial training

Abstract

Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-supervised learning methods propose combinatorial CNN-Transformer learning to cross teach a transformer with a convolutional neural network, which achieves promising results. However, it remains a challenging task to effectively train the transformer with limited labeled data. In this paper, we propose an adversarial masked image modeling method to fully unleash the potential of transformer for semi-supervised medical image segmentation. The key challenge in semi-supervised learning with transformer lies in the lack of sufficient supervision signal. To this end, we propose to construct an auxiliary masked domain from original domain with masked image modeling and train the transformer to predict the entire segmentation mask with masked inputs to increase supervision signal. We leverage the original labels from labeled data and pseudo-labels from unlabeled data to learn the masked domain. To further benefit the original domain from masked domain, we provide a theoretical analysis of our method from a multi-domain learning perspective and devise a novel adversarial training loss to reduce the domain gap between the original and masked domain, which boosts semi-supervised learning performance. We also extend adversarial masked image modeling to CNN network. Extensive experiments on three public medical image segmentation datasets demonstrate the effectiveness of our method, where our method outperforms existing methods significantly. Our code is publicly available at https://github.com/zlheui/AdvMIM.