A harmonization framework enables comparison of six AI segmentation models on 31 structures in NLST CT scans, revealing strong agreement for lungs but invalid outputs for some vertebrae and ribs.
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XRePIT automates residual-guided switching between neural surrogates and OpenFOAM to enable stable, up to 2.91x faster 3D unsteady flow simulations with L2 errors around 1E-03.
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In search of truth: Evaluating concordance of AI-based anatomy segmentation models
A harmonization framework enables comparison of six AI segmentation models on 31 structures in NLST CT scans, revealing strong agreement for lungs but invalid outputs for some vertebrae and ribs.
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XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
XRePIT automates residual-guided switching between neural surrogates and OpenFOAM to enable stable, up to 2.91x faster 3D unsteady flow simulations with L2 errors around 1E-03.