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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

4 Sep 2024arXiv:2409.03062archive 2025-07-28

Shehan Perera, Yunus Erzurumlu, Deepak Gulati, Alper Yilmaz

Skin cancer segmentation poses a significant challenge in medical image analysis. Numerous existing solutions, predominantly CNN-based, face issues related to a lack of global contextual understanding. Alternatively, some approaches resort to large-scale Transformer models to bridge the global contextual gaps, but at the expense of model size and computational complexity. Finally many Transformer based approaches rely primarily on CNN based decoders overlooking the benefits of Transformer based decoding models. Recognizing these limitations, we address the need efficient lightweight solutions by introducing MobileUNETR, which aims to overcome the performance constraints associated with both CNNs and Transformers while minimizing model size, presenting a promising stride towards efficient image segmentation. MobileUNETR has 3 main features. 1) MobileUNETR comprises of a lightweight hybrid CNN-Transformer encoder to help balance local and global contextual feature extraction in an efficient manner; 2) A novel hybrid decoder that simultaneously utilizes low-level and global features at different resolutions within the decoding stage for accurate mask generation; 3) surpassing large and complex architectures, MobileUNETR achieves superior performance with 3 million parameters and a computational complexity of 1.3 GFLOP resulting in 10x and 23x reduction in parameters and FLOPS, respectively. Extensive experiments have been conducted to validate the effectiveness of our proposed method on four publicly available skin lesion segmentation datasets, including ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. The code will be publicly available at: https://github.com/OSUPCVLab/MobileUNETR.git

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Code

osupcvlab/mobileunetr officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Image Segmentation Lesion Segmentation Medical Image Analysis Medical Image Segmentation Semantic Segmentation Skin Cancer Segmentation Skin Lesion Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Lesion Segmentation	ISIC 2018	MobileUNETR	Mean IoU	0.8456	#7 of 17	Archive leaderboard	report
Lesion Segmentation	ISIC 2018	MobileUNETR	mean Dice	0.9074	#7 of 17	Archive leaderboard	report
Lesion Segmentation	PH2	MobileUNETR	Dice Score	0.9570	#2 of 3	Archive leaderboard	report
Medical Image Segmentation	ISIC2018	MobileUNETR	Accuracy	94.40	#1 of 3	Archive leaderboard	report
Medical Image Segmentation	ISIC2018	MobileUNETR	mean Dice	90.74	#1 of 3	Archive leaderboard	report
Semantic Segmentation	PH2	MobileUNETR	Average Dice	95.70	#1 of 2	Archive leaderboard	report
Semantic Segmentation	PH2	MobileUNETR	Average IOU	92.30	#1 of 2	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 Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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