A clinically informed coarse-to-fine multimodal deep learning model improves longitudinal CT registration for proton therapy by incorporating contours, dose, and text priors.
arXiv preprint arXiv:2411.02372 (2024)
2 Pith papers cite this work. Polarity classification is still indexing.
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A pipeline using segmentation, atlas registration, radiomics, and geometric features achieves 87.5% CVD classification accuracy on ASOCA, outperforming direct raw-image classification at 67.5%.
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A Multimodal Clinically Informed Coarse-to-Fine Framework for Longitudinal CT Registration in Proton Therapy
A clinically informed coarse-to-fine multimodal deep learning model improves longitudinal CT registration for proton therapy by incorporating contours, dose, and text priors.
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Cardiovascular disease classification using radiomics and geometric features from cardiac CT
A pipeline using segmentation, atlas registration, radiomics, and geometric features achieves 87.5% CVD classification accuracy on ASOCA, outperforming direct raw-image classification at 67.5%.