6 papers
HEARTS: Benchmarking LLM Reasoning on Health Time Series
Sirui Li, Shuhan Xiao, Mihir Joshi +4
The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of hea…
nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
Alexandra Ertl, Stefan Denner, Robin Peretzke +8
Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…
Leveraging Foundation Models for Content-Based Image Retrieval in Radiology
Stefan Denner, David Zimmerer, Dimitrios Bounias +8
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. However, current CBIR systems face limitations due…
Enhancing predictive imaging biomarker discovery through treatment effect analysis
Shuhan Xiao, Lukas Klein, Jens Petersen +3
Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. The…
From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging
Maximilian Rokuss, Balint Kovacs, Yannick Kirchhoff +4
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological va…
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge
Balint Kovacs, Shuhan Xiao, Maximilian Rokuss +3
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images thr…