DuoMod-Net targets class imbalance in semi-supervised organ segmentation with dual-component design

Semi-supervised segmentation of minority organs improves with DuoMod-Net, which decouples background gradient bias (RLM) and expands decision boundaries during training (DAFR). Gains on tail classes and zero-shot generalization reported across 5–20% labeled-data regimes (exact m…

• Algorithm study validating DuoMod-Net across three labeled-data fractions (5%, 10%, 20%); sample size and specific DSC/HD95 values not reported in the abstract.
• Two components work in tandem: logarithmic gradient re-weighting (RLM) anchors background as a neutral pivot, while disagreement-driven feature expansion (DAFR) creates a geometric safety margin removed at inference to reduce catastrophic failures.
• Key limitation: No external prospective validation described; zero-shot generalization claim is based on unseen datasets within the study's own evaluation framework, and independent external validation has not been reported.

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