ProSMA-UNet replaces U-Net skip gating with an ℓ1 proximal sparse gate, reporting best results across 2D and 3D medical segmentation benchmarks and about a 19% F1 gain on 3D colon.
The work proposes ProSMA-UNet, which recasts skip connections in U-shaped medical segmentation networks as a decoder-conditioned sparse feature selection problem: it builds a multi-scale encoder-decoder compatibility field with lightweight depthwise dilated convolutions, applies an ℓ1 proximal operator with learnable per-channel thresholds to yield a closed-form soft-thresholding gate, and adds decoder-conditioned channel gating driven by global decoder context, reporting best results on three 2D benchmarks (BUSI, GlaS, Kvasir-SEG) and two 3D benchmarks (Spleen and Colon from the Medical Segmentation Decathlon), with roughly a 19% relative F1 gain over the strongest baseline on Colon.