622 citations · 636 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Matthias Perkonigg, Johannes Hofmanninger, Christian Herold +2
Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition setti…
Separation of target anatomical structure and occlusions in chest radiographs
Johannes Hofmanninger, Sebastian Roehrich, Helmut Prosch +1
Chest radiographs are commonly performed low-cost exams for screening and diagnosis. However, radiographs are 2D representations of 3D structures causing considerable clutter imped…
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
Johannes Hofmanninger, Florian Prayer, Jeanny Pan +3
Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exist, involving sophisti…