1k citations · 1.1k across the 11 of their papers we have counts for
12 papers
GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
Yash Shah, Omar Todd, Philipp Seeböck +3
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…
LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI
Mohammed Kamran, Maria Bernathova, Raoul Varga +5
Accurate segmentation of small lesions in Breast Dynamic Contrast-Enhanced MRI (DCE-MRI) is critical for early cancer detection, especially in high-risk patients. While recent deep…
Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Ajay Mittal, Raghav Mehta, Omar Todd +3
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decision…
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…
Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching
Stefan Brandstätter, Philipp Seeböck, Christoph Fürböck +3
2D to 3D registration is essential in tasks such as diagnosis, surgical navigation, environmental understanding, navigation in robotics, autonomous systems, or augmented reality. I…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael Roberts, Derek Driggs, Matthew Thorpe +13
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (…