AI RESEARCH
LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors
arXiv CS.LG
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ArXi:2603.07228v1 Announce Type: new Accurate and efficient 3D medical image segmentation is essential for clinical AI, where models must remain reliable under stringent memory, latency, and data availability constraints. Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization. We propose LightMedSeg, a modular UNet-style segmentation architecture that integrates anatomical priors with adaptive context modeling.