Simple is what you need for efficient and accurate medical image segmentation
Journal:
arXiv
Published Date:
Jun 16, 2025
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.
