CurvSegFlow applies time-conditioned flow matching with a U-Net backbone and triple-term loss to progressively refine segmentations of thin structures in noisy images, reporting competitive performance on microtubule, vessel, and nerve datasets.
Laddernet: Multi-path networks based on u-net for medical image segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DW-Net improves the accuracy versus computational cost Pareto front over standard U-Nets for 2D and 3D multi-scale flow benchmarks by stacking multiple waves while keeping training settings identical.
citing papers explorer
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CurvSegFlow: Time-Conditioned Flow Matching for Robust Segmentation of Curvilinear Structures in Noisy Biomedical Images
CurvSegFlow applies time-conditioned flow matching with a U-Net backbone and triple-term loss to progressively refine segmentations of thin structures in noisy images, reporting competitive performance on microtubule, vessel, and nerve datasets.
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Deep Wave Network for Modeling Multi-Scale Physical Dynamics
DW-Net improves the accuracy versus computational cost Pareto front over standard U-Nets for 2D and 3D multi-scale flow benchmarks by stacking multiple waves while keeping training settings identical.