  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.
