activity
20202026
most citedCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

1k citations · 1.1k across the 11 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

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

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…

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…

cs.CV2025

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…

cs.CV2025

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…

eess.IV2025

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…