Prof. Dr. Bernhard Kainz

Professur für Image Data Exploration and Analysis

Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks (2023) Baugh M, Tan J, Müller JP, Dombrowski M, Batten J, Kainz B Conference contribution, Original article Remote Expert DVT Triaging of Novice-User Compression Sonography with AI-Guidance (2023) Oppenheimer J, Mandegaran R, Staabs F, Adler A, Singöhl S, Kainz B, Heinrich M, et al. Journal article Learnable Slice-to-volume Reconstruction for Motion Compensation in Fetal Magnetic Resonance Imaging (2023) Jehn C, Müller J, Kainz B Conference contribution Keynote: Beyond Supervised Learning Exploring Novel Machine Learning Approaches for Robust Medical Image Analysis (2023) Kainz B Conference contribution MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images (2022) Zimmerer D, Full PM, Isensee F, Jaeger P, Adler T, Petersen J, Koehler G, et al. Journal article Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time (2022) Matthew J, Skelton E, Day TG, Zimmer VA, Gomez A, Wheeler G, Toussaint N, et al. Journal article Fetal Cortex Segmentation with Topology and Thickness Loss Constraints (2022) Li L, Ma Q, Li Z, Ouyang C, Zhang W, Price A, Kyriakopoulou V, et al. Conference contribution Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization (2022) Schlüter HM, Tan J, Hou B, Kainz B Conference contribution A variational Bayesian method for similarity learning in non-rigid image registration (2022) Grzech D, Azampour MF, Glocker B, Schnabel J, Navab N, Kainz B, Folgoc LL Conference contribution nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods (2022) Baugh M, Tan J, Vlontzos A, Müller J, Kainz B Conference contribution