8 papers · 1 filter
GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
Yash Shah, Omar Todd, Philipp Seeböck +4
The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in clinical practice. Recently, a hybrid pip…
MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images
Felicia Bader, Philipp Seeböck, Anastasia Bartashova +2
In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are meaningful in their anatomica…
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…
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
Jeanny Pan, Philipp Seeböck, Christoph Fürböck +5
Identifying new disease-related patterns in medical imaging data with the help of machine learning enlarges the vocabulary of recognizable findings. This supports diagnostic and pr…
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…
Semantic Mosaicing of Histo-Pathology Image Fragments using Visual Foundation Models
Stefan Brandstätter, Maximilian Köller, Philipp Seeböck +5
In histopathology, tissue samples are often larger than a standard microscope slide, making stitching of multiple fragments necessary to process entire structures such as tumors. A…