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
The NV-Reason-CT model, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning, and the model and training code are released to support reproducible research on explainable AI for volumetric medical imaging.
Andriy Myronenko, Dong Yang, Yu-Cheng Tang et al.· 0 citations
This work proposes Projected Energy Matching, a scalable framework that resolves structural and computational bottlenecks in energy matching, and introduces Helmholtz Distillation, a structural relaxation that leverages a Hutchinson trace estimator to explicitly absorb rotational noise into an auxiliary residual networ...
Daniel Barco, M. Balcerak, Suprosanna Shit et al.· 0 citations
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