Prof. Dr.-Ing. Katharina Breininger

Juniorprofessur für Artificial Intelligence in Medical Imaging Department Artificial Intelligence in Biomedical Engineering

Appearance-based Debiasing of Deep Learning Models in Medical Imaging (2024) Wilm F, Reimann MK, Taubmann O, Mühlberg A, Breininger K Conference contribution, Conference Contribution Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning (2024) Ganz J, Puget C, Ammeling J, Parlak E, Kiupel M, Bertram CA, Breininger K, et al. Conference contribution, Conference Contribution Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors (2024) Aubreville M, Pan Z, Sievert M, Ammeling J, Ganz J, Oetter N, Stelzle F, et al. Conference contribution, Conference Contribution Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks (2024) Rivera Monroy L, Rist L, Eberhardt M, Ostalecki C, Bauer A, Vera J, Breininger K, Maier A Conference contribution Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability (2024) Wang R, Bradley C, Herbert P, Hou K, Hager GD, Breininger K, Unberath M, et al. Journal article Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision (2024) Sievert M, Aubreville M, Müller SK, Eckstein M, Breininger K, Iro H, Goncalves M Journal article A comprehensive multi-domain dataset for mitotic figure detection (2023) Aubreville M, Wilm F, Stathonikos N, Breininger K, Donovan TA, Jabari S, Veta M, et al. Journal article Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation (2023) Qiu J, Wilm F, Öttl M, Schlereth M, Liu C, Heimann T, Aubreville M, Breininger K Conference contribution, Conference Contribution Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making (2023) Ammeling J, Manger C, Kwaka E, Krügel S, Uhl M, Kießig A, Fritz A, et al. Conference contribution Handling Label Uncertainty on the Example of Automatic Detection of Shepherd’s Crook RCA in Coronary CT Angiography (2023) Denzinger F, Wels M, Taubmann O, Kordon F, Wagner F, Mehltretter S, Gülsün MA, et al. Conference contribution