Volumetric computed tomography (CT) segmentation supports quantitative image analysis, but most AI-based methods remain organ-specific, with fragmented models, inconsistent label definitions, and heterogeneous training and evaluation protocols. Here we present CADS, a unified data–model framework for scalable and gen...
Murong Xu, Fernando Navarro, Maksym Fritsak et al.· Nature Communications· 0 citations
We study the generation of synthetic longitudinal patient records as FHIR graphs, representing each patient as a sequence of encounters connected to diagnoses and procedures. Faithful generation requires jointly modeling the temporal structure and node content under extreme long-tail distributions of clinical event cod...
M. Bahn, Bahadır Eryılmaz, Henning Schäfer et al.· IEEE Access· 0 citations
BACKGROUND
Although Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently incon...
Yu-Tong Wen, A. Quinsten, C. S. Schmidt et al.· JMIR Medical Informatics· 0 citations
Deep Learning-based analysis of CT topograms with DICOM-derived spatial geometry, enables reliable and reproducible image-based anatomical labelling of CT series, reducing reliance on inconsistent DICOM attributes and improving data consistency and scalability for clinical applications.
Yu-Tong Wen, Judith Kohnke, V. Parmar et al.· European Radiology· 1 citation
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