Simple is what you need for efficient and accurate medical image segmentation

• URL: http://arxiv.org/abs/2506.13415v1
• Date: Mon, 16 Jun 2025 12:31:48 GMT
• Title: Simple is what you need for efficient and accurate medical image segmentation
• Authors: Xiang Yu, Yayan Chen, Guannan He, Qing Zeng, Yue Qin, Meiling Liang, Dandan Luo, Yimei Liao, Zeyu Ren, Cheng Kang, Delong Yang, Bocheng Liang, Bin Pu, Ying Yuan, Shengli Li,
• Abstract summary: This paper presents SimpleUNet, a scalable ultra-lightweight medical image segmentation model.<n>With a record-breaking 16 KB parameter configuration, SimpleUNet outperforms LBUNet and other lightweight benchmarks.<n>The 0.67 MB variant achieves superior efficiency (8.60 GFLOPs) and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on multi-center breast lesion datasets.
• Score: 7.2109224546543675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While modern segmentation models often prioritize performance over practicality, we advocate a design philosophy prioritizing simplicity and efficiency, and attempted high performance segmentation model design. This paper presents SimpleUNet, a scalable ultra-lightweight medical image segmentation model with three key innovations: (1) A partial feature selection mechanism in skip connections for redundancy reduction while enhancing segmentation performance; (2) A fixed-width architecture that prevents exponential parameter growth across network stages; (3) An adaptive feature fusion module achieving enhanced representation with minimal computational overhead. With a record-breaking 16 KB parameter configuration, SimpleUNet outperforms LBUNet and other lightweight benchmarks across multiple public datasets. The 0.67 MB variant achieves superior efficiency (8.60 GFLOPs) and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on multi-center breast lesion datasets, surpassing both U-Net and TransUNet. Evaluations on skin lesion datasets (ISIC 2017/2018: mDice 84.86%/88.77%) and endoscopic polyp segmentation (KVASIR-SEG: 86.46%/76.48% mDice/mIoU) confirm consistent dominance over state-of-the-art models. This work demonstrates that extreme model compression need not compromise performance, providing new insights for efficient and accurate medical image segmentation. Codes can be found at https://github.com/Frankyu5666666/SimpleUNet.

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