Papers › MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

30 Oct 2023arXiv:2310.19898archive 2025-07-28

Md Motiur Rahman, Shiva Shokouhmand, Smriti Bhatt, Miad Faezipour

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.

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Code

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Tasks

Decoder Image Segmentation Medical Image Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Medical Image Segmentation	Automatic Cardiac Diagnosis Challenge (ACDC)	MIST	Avg DSC	92.56	#5 of 20	Archive leaderboard	report
Medical Image Segmentation	Synapse multi-organ CT	MIST	Avg DSC	86.92	#6 of 23	Archive leaderboard	report
Medical Image Segmentation	Synapse multi-organ CT	MIST	Avg HD	11.07	#6 of 23	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE CAM Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Vision Transformer

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