3 papers
cs.CV2025
AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring
Branko Mitic, Philipp Seeböck, Helmut Prosch +1
Early detection of newly emerging diseases, lesion severity assessment, differentiation of medical conditions and automated screening are examples for the wide applicability and im…
cs.CV2025
No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data
Christoph Fürböck, Paul Weiser, Branko Mitic +3
In real world clinical environments, training and applying deep learning models on multi-modal medical imaging data often struggles with partially incomplete data. Standard approac…
eess.IV2024
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns
Branko Mitic, Philipp Seeböck, Jennifer Straub +2
Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve famil…