Heiko Hillenhagen

Abstract: Your other Left! Vision-language Models Fail to Understand Relative Positions in Medical Images

Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images

A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans

Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generation and structured image understanding. While...

Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging

Numerous unsupervised domain adaptation (UDA) algorithms exist, but for clinical practice, selecting the best-suited one along with proper hyperparameters often remains unclear, as the unlabeled...

How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging

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 (target) domain is...

Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images

Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical...