LightMIS：面向边缘设备的超轻量医学图像分割模型
LightMIS: Ultra-lightweight medical image segmentation for edge devices [R]
LightMIS 以 131K 参数实现 2D 二值医学图像分割，直接聚合多尺度编码器特征、无需逐阶段学习解码器，在相同 nnU-Net 协议下取得与 nnWNet 接近的模态宏平均 Dice 和 IoU，参数量少 98.14%、GFLOPs 少 94.58%。
Hi everyone,
I am one of the authors of LightMIS, and I would like to share our work on 2D binary medical image segmentation. LightMIS has 131K parameters and aggregates multi-scale encoder features directly, without a learned stage-wise decoder. It achieves close observed modality-macro Dice and IoU averages to nnWNet, while using 98.14% fewer parameters and 94.58% fewer GFLOPs, with both models trained and evaluated under the same nnU-Net protocol.
Paper: https://arxiv.org/abs/2609.28327
Code: https://github.com/AndreiiArhire/LightMIS
We also tested LightMIS and the reference models on a real edge device with an Arm Mali-G52 MC2 GPU, where the full LightMIS model ran around 10.5× faster than nnWNet. All LightMIS variants achieved 100% GPU operator coverage, without CPU fallback.
We started by training several popular medical image segmentation architectures, both lightweight and heavyweight, from scratch in the same controlled environment using nnU-Net v2.3.1. The experiments covered six datasets and five imaging modalities. Our goal was to develop a lightweight architecture that performs well across these modalities, which led us to the current version of LightMIS.
The source code, checkpoints, and details needed to reproduce the results are publicly available. Feel free to experiment with it and try it on your own segmentation tasks. I would be happy to hear how it works for you! Any suggestions or feedback are welcome!
submitted by /u/OkLion2068
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来源：r/MachineLearning · reddit.com