Accurate segmentation of anatomical structures and abnormalities in medical
images is crucial for computer-aided diagnosis and analysis. While deep
learning techniques excel at this task, their computational demands pose
challenges. Additionally, some cutting-edge segmentation methods, though
effective for general object segmentation, may not be optimised for medical
images. To address these issues, we propose Mini-Net, a lightweight
segmentation network specifically designed for medical images. With fewer than
38,000 parameters, Mini-Net efficiently captures both high- and low-frequency
features, enabling real-time applications in various medical imaging scenarios.
We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016,
ISIC-2018, and MoNuSeg, demonstrating its robustness and good performance
compared to state-of-the-art methods.

我们提出了 Mini-Net，一个专门为医学图像设计的轻量级分割网络，通过少于 38,000 个参数高效捕捉高频和低频特征，从而实现在各种医学成像场景中的实时应用，并在 DRIVE、STARE、ISIC-2016、ISIC-2018 和 MoNuSeg 等各种数据集上评估了 Mini-Net，展现了它与最先进方法相比的稳健性和良好性能。