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

While modern segmentation models often prioritize performance over practicality, we advocate for a design philosophy that prioritizes simplicity and efficiency, and strive to design high-performance segmentation models. This paper presents SimpleUNet, a scalable, lightweight medical image segmentation framework. The key is that we proposed a simple yet effective partial feature selection mechanism for reducing information redundancy and thus facilitating compact model design. Additionally, we found that adjusting the model width is a straightforward yet easily overlooked tactic for lightweight model design, thereby preventing exponential parameter growth across network stages. By integrating an almost parameter-free channel attention module, the performance of the developed models can be improved with minimal overhead. Leveraging these techniques, our record-breaking model SimpleUNet with only 16 KB parameters surpasses LBUNet and other lightweight benchmarks across multiple public datasets. Impressively, the 0.67 MB variant achieves superior efficiency and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on a curated multi-center breast lesion dataset, 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. Although our current SimpleUNet architecture does not rely on exotic or custom operators, it is fundamentally designed to embrace future innovations. The framework remains fully compatible with emerging operator-level advancements, allowing effortless integration and seamless upgrades without structural modifications. Codes can be found at https://github.com/Frankyu5666666/SimpleUNet .