55 citations · 55 across the 5 of their papers we have counts for
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A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans
Simon Vincent Abel, Heiko Hillenhagen, Michael Götz +3
Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generation and structured image under…
Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging
Yiheng Xiong, Luisa Gallée, Daniel Santak Wolf +2
Numerous unsupervised domain adaptation (UDA) algorithms exist, but for clinical practice, selecting the best-suited one along with proper hyperparameters often remains unclear, as…
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
Yiheng Xiong, Luisa Gallée, Daniel Santak Wolf +2
Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship. However, the deployment (targe…
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images
Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin +4
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in…
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…