Author

Francesco Di Salvo

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Preprint Aug 2026

TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection

Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive. This translational gap stems in part from a structural flaw in model development: the reliance on curated datasets that under-represent the long negative stretches and proce...

Sebastian Doerrich, A. Schwab, Francesco Di Salvo et al. · 0 citations

Jul 2026

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

MoPET, a mixture-of-experts (MoE) method that uses a learned sparse router to direct each input through a small subset of low-rank PEFT experts injected into a frozen foundation model, sharing capacity across datasets while limiting cross-domain gradient conflict is proposed.

Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo et al. · arXiv.org · 0 citations