One of the common and promising deep learning approaches used for medical
image segmentation is transformers, as they can capture long-range dependencies
among the pixels by utilizing self-attention. Despite being successful in
medical image segmentation, transformers face limitations in capturing local
contexts of pixels in multimodal dimensions. We propose a Medical Image
Segmentation Transformer (MIST) incorporating a novel Convolutional Attention
Mixing (CAM) decoder to address this issue. MIST has two parts: a pre-trained
multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded
feature representation is passed through the CAM decoder for segmenting the
images. In the CAM decoder, an attention-mixer combining multi-head
self-attention, spatial attention, and squeeze and excitation attention modules
is introduced to capture long-range dependencies in all spatial dimensions.
Moreover, to enhance spatial information gain, deep and shallow convolutions
are used for feature extraction and receptive field expansion, respectively.
The integration of low-level and high-level features from different network
stages is enabled by skip connections, allowing MIST to suppress unnecessary
information. The experiments show that our MIST transformer with CAM decoder
outperforms the state-of-the-art models specifically designed for medical image
segmentation on the ACDC and Synapse datasets. Our results also demonstrate
that adding the CAM decoder with a hierarchical transformer improves
segmentation performance significantly. Our model with data and code is
publicly available on GitHub.

提出了一种 Medical Image Segmentation Transformer (MIST) 用于医学图像分割，其中包括一个新型的 Convolutional Attention Mixing (CAM) decoder，通过 skip connections 抑制不必要的信息，实验结果在 ACDC 和 Synapse 数据集上显示，我们的 MIST transformer with CAM decoder 优于专门设计用于医学图像分割的最先进模型。