Class-Aware Adversarial Transformers for Medical Image Segmentation

TL;DR

CASTformer introduces a class-aware adversarial transformer with multi-scale features for improved medical image segmentation, significantly outperforming previous models by capturing rich semantic and anatomical details.

Contribution

The paper proposes CASTformer, a novel adversarial transformer with a pyramid structure and class-aware modules, enhancing segmentation accuracy in medical images.

Findings

Outperforms previous state-of-the-art transformer models by 2.54%-5.88% in Dice score.

Utilizes multi-scale representations to handle variations in medical images.

Transfer learning improves performance and reduces dataset size requirements.

Abstract

Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain. However, current transformer-based models suffer from several disadvantages: (1) existing methods fail to capture the important features of the images due to the naive tokenization scheme; (2) the models suffer from information loss because they only consider single-scale feature representations; and (3) the segmentation label maps generated by the models are not accurate enough without considering rich semantic contexts and anatomical textures. In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation. First, we take advantage of the pyramid structure to construct multi-scale representations and handle multi-scale variations. We then design a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures. Lastly, we utilize an adversarial training strategy that boosts segmentation accuracy and correspondingly allows a transformer-based discriminator to capture high-level semantically correlated contents and low-level anatomical features. Our experiments demonstrate that CASTformer dramatically outperforms previous state-of-the-art transformer-based approaches on three benchmarks, obtaining 2.54%-5.88% absolute improvements in Dice over previous models. Further qualitative experiments provide a more detailed picture of the model's inner workings, shed light on the challenges in improved transparency, and demonstrate that transfer learning can greatly improve performance and reduce the size of medical image datasets in training, making CASTformer a strong starting point for downstream medical image analysis tasks.