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Department Artificial Intelligence in Biomedical Engineering (AIBE)
Friedrich-Alexander-Universität Erlangen-Nürnberg
Technische Fakultät
Overview
Publications
(1,302)
Research Data
(11)
Research Grants
(4)
Types of publications
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Journal article
Journal article
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Book chapter / Article in edited volumes
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Conference contribution
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Image-conditioned latent rectified flow models for 3D medical anomaly localisation (2026)
Baugh M, Müller J, Cechnicka S, Gu Baugh K, Bonnici J, Myles J, Kainz B
Journal article
Self-adaptive forward-forward network for anomaly detection and medical image analysis (2026)
Müller J, Baugh M, Kainz B
Journal article
Predicting Numeric Hemoglobin A1c for Individualized Type 2 Diabetes Care Using Machine Learning: A MIMIC-IV Study (2026)
Habibpour M, Tanzanakis A, Salin E, Eskofier B
Conference contribution
Zwischen Schmerz und Stress: Ein integriertes Paradigma zur Untersuchung von Stress, Bewegungsmustern und Fear Avoidance unter simulierten Rückenschmerzen (2026)
Heuler A, Oeler C, Richer R, Rohleder N, Barke A, Weise C
Conference contribution, Abstract of lecture
Multimodal Assessment of Pain-Related Behavioral and Biopsychological Changes in the Context of Back Pain Induction (2026)
Heuler A, Oeler C, Richer R, Rohleder N, Barke A, Weise C
Conference contribution, Abstract of a poster
A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion-Weighted Imaging Using Denoising Auto-Encoder (2026)
Glaser J, Tan Z, Hofmann A, Laun FB, Knoll F
Journal article
LUMOS: Self-supervised Pretraining for Multimodal Task Load Estimation in Adaptive Instructional Systems (2026)
Oppelt MP, Lang-Richter NR, Eskofier B
Conference contribution
MEASURING AND ALIGNING ABSTRACTION IN VISION-LANGUAGE MODELS WITH MEDICAL TAXONOMIES (2026)
Schaper B, Di Folco M, Kainz B, Schnabel JA, Bercea CI
Conference contribution
Characterization of Foundation Models for Longitudinal Similarity Measurement in Medical Video Data (2026)
Neubig L, Larsen D, Ikuma T, Kunduk M, Kist A
Conference contribution
Quantifying Anatomical Bias in Coronary Segmentation: Why Your Model Prefers the LCA More Than the RCA (2026)
Baumgart S, Deubner N, Kist A
Conference contribution
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