12 citations · 15 across the 15 of their papers we have counts for
8 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…
Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation
Shyam Nandan Rai, Francesco Di Salvo, Sebastian Doerrich +1
Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts arise across institutions, acquisition protocols, and patient populati…
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…
An Embedding is Worth a Thousand Noisy Labels
Francesco Di Salvo, Sebastian Doerrich, Ines Rieger +1
The performance of deep neural networks scales with dataset size and label quality, rendering the efficient mitigation of low-quality data annotations crucial for building robust a…
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
Sebastian Doerrich, Tobias Archut, Francesco Di Salvo +1
Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user da…