12 citations · 15 across the 14 of their papers we have counts for
7 papers · 1 filter
Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching
Sebastian Doerrich, Francesco Di Salvo, Shyam Nandan Rai +2
Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall…
TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection
Sebastian Doerrich, Andreas Franz Schwab, Francesco Di Salvo +3
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 p…
MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo +2
Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end…
Vertical Fusion: Condensing Internal Representations for Robust ViT Classification
Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi +4
Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is conside…
MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring
Francesco Di Salvo, Sebastian Doerrich, Christian Ledig
Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from changes in patients, devices,…
HypCBC: Domain-Invariant Hyperbolic Cross-Branch Consistency for Generalizable Medical Image Analysis
Francesco Di Salvo, Sebastian Doerrich, Jonas Alle +1
Robust generalization beyond training distributions remains a critical challenge for deep neural networks. This is especially pronounced in medical image analysis, where data is of…
Stylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization
Sebastian Doerrich, Francesco Di Salvo, Jonas Alle +1
Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmen…