Optimizing Vision Transformers for Medical Image Segmentation
This work addresses the challenge of poor generalization of Transformers on small medical imaging datasets, offering a more accurate and parameter-efficient solution for medical image segmentation tasks.
The paper tackles the problem of Vision Transformers lacking spatial and local context for medical image segmentation by introducing CS-Unet, a compact network that integrates convolutions with Transformers, achieving state-of-the-art performance on multi-organ and cardiac datasets without pre-training.
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations. However, existing research uses off-the-shelf vision Transformer blocks based on linear projections and feature processing which lack spatial and local context to refine organ boundaries. Furthermore, Transformers do not generalize well on small medical imaging datasets and rely on large-scale pre-training due to limited inductive biases. To address these problems, we demonstrate the design of a compact and accurate Transformer network for MISS, CS-Unet, which introduces convolutions in a multi-stage design for hierarchically enhancing spatial and local modeling ability of Transformers. This is mainly achieved by our well-designed Convolutional Swin Transformer (CST) block which merges convolutions with Multi-Head Self-Attention and Feed-Forward Networks for providing inherent localized spatial context and inductive biases. Experiments demonstrate CS-Unet without pre-training outperforms other counterparts by large margins on multi-organ and cardiac datasets with fewer parameters and achieves state-of-the-art performance. Our code is available at Github.