Jiaying Li

The authors propose MAGEFormer, which embeds physical spacing constraints into representation learning: MASE calibrates positional frequencies by voxel spacing, GCA suppresses physically implausible cross-scale feature correlations, and GVV performs deterministic sub-voxel view aggregation at inference; under a unified 5-fold protocol on BTCV and FLARE 22 it attains the strongest boundary accuracy among compared methods (10.58 mm and 3.40 mm HD95) with consistent Dice gains, and its advantage grows with anisotropy severity.

The authors propose MAGEFormer, which embeds physical spacing constraints into representation learning: MASE calibrates positional frequencies by voxel spacing, GCA suppresses physically implausible cross-scale feature correlations, and GVV performs deterministic sub-voxel view aggregation at inference; under a unified 5-fold protocol on BTCV and FLARE 22 it attains the strongest boundary accuracy among compared methods (10.58 mm and 3.40 mm HD95) with consistent Dice gains, and its advantage grows with anisotropy severity.