M-Net folds differentiable condition-number, divergence, and curl-like features into U-Net and reports Dice gains of 3.52 to 12.37 percentage points over a U-Net baseline on liver, kidney, and brain tumor segmentation.
Leveraging matrix invertibility as features in neural networks for medical image segmentation.In preparation, 2024
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M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation
M-Net folds differentiable condition-number, divergence, and curl-like features into U-Net and reports Dice gains of 3.52 to 12.37 percentage points over a U-Net baseline on liver, kidney, and brain tumor segmentation.