A deep learning network performs non-rigid volume-to-surface registration to recover dense displacement fields from partial brain surface point clouds, achieving 1.13 mm endpoint error for brain shift compensation.
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ArGEnT adds self-, cross-, and hybrid-attention transformers to DeepONet to learn geometry-dependent operators from point-cloud inputs, yielding higher accuracy than standard DeepONet on fluid, solid, and electrochemical benchmarks.
Random forest predicting prosthetist adaptations from limb scans achieves median surface-to-surface error of 1.24 mm, outperforming direct socket shape prediction and other models.
citing papers explorer
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Deep learning based Non-Rigid Volume-to-Surface Registration for Brain Shift compensation Using Point Cloud
A deep learning network performs non-rigid volume-to-surface registration to recover dense displacement fields from partial brain surface point clouds, achieving 1.13 mm endpoint error for brain shift compensation.
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ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
ArGEnT adds self-, cross-, and hybrid-attention transformers to DeepONet to learn geometry-dependent operators from point-cloud inputs, yielding higher accuracy than standard DeepONet on fluid, solid, and electrochemical benchmarks.
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Evaluating Artificial Intelligence Algorithms for the Standardization of Transtibial Prosthetic Socket Shape Design
Random forest predicting prosthetist adaptations from limb scans achieves median surface-to-surface error of 1.24 mm, outperforming direct socket shape prediction and other models.