Papers › MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

15 May 2023arXiv:2305.08396archive 2025-07-28

Abdul Rehman Khan, Asifullah Khan

Since their emergence, Convolutional Neural Networks (CNNs) have made significant strides in medical image analysis. However, the local nature of the convolution operator may pose a limitation for capturing global and long-range interactions in CNNs. Recently, Transformers have gained popularity in the computer vision community and also in medical image segmentation due to their ability to process global features effectively. The scalability issues of the self-attention mechanism and lack of the CNN-like inductive bias may have limited their adoption. Therefore, hybrid Vision transformers (CNN-Transformer), exploiting the advantages of both Convolution and Self-attention Mechanisms, have gained importance. In this work, we present MaxViT-UNet, a new Encoder-Decoder based UNet type hybrid vision transformer (CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage with a nominal memory and computational burden. The inclusion of multi-axis self-attention, within each decoder stage, significantly enhances the discriminating capacity between the object and background regions, thereby helping in improving the segmentation efficiency. In the Hybrid Decoder, a new block is also proposed. The fusion process commences by integrating the upsampled lower-level decoder features, obtained through transpose convolution, with the skip-connection features derived from the hybrid encoder. Subsequently, the fused features undergo refinement through the utilization of a multi-axis attention mechanism. The proposed decoder block is repeated multiple times to segment the nuclei regions progressively. Experimental results on MoNuSeg18 and MoNuSAC20 datasets demonstrate the effectiveness of the proposed technique.

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Code

prlab21/maxvit-unet officialmentioned in papermentioned on GitHubpytorch report

abdul2706/MaxViT-UNet officialmentioned on GitHubpytorch report

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Tasks

Decoder Flood extent forecasting Image Segmentation Inductive Bias Medical Image Analysis Medical Image Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Flood extent forecasting	Global Flood forecasting	MaxViT U-Net	F1 score	0.75	#4 of 5	Archive leaderboard	report
Medical Image Segmentation	MoNuSAC	MaxViT-UNet	Dice	0.8215	#1 of 1	Archive leaderboard	report
Medical Image Segmentation	MoNuSAC	MaxViT-UNet	IoU	0.7030	#1 of 1	Archive leaderboard	report
Medical Image Segmentation	MoNuSeg 2018	MaxViT-UNet	Dice	0.8378	#1 of 1	Archive leaderboard	report
Medical Image Segmentation	MoNuSeg 2018	MaxViT-UNet	IoU	0.7208	#1 of 1	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 Convolution 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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