March 13, 2024, 4:43 a.m. | Julian Suk, Baris Imre, Jelmer M. Wolterink

cs.LG updates on

arXiv:2403.07536v1 Announce Type: cross
Abstract: Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates unique challenges in building deep neural network architectures. Furthermore, patient-specific meshes may not be canonically aligned which limits the generalisation of machine learning algorithms. We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with …

3d models abstract algebra architectures arxiv biomedical building challenges cs.lg deep neural network extract fidelity however information lab machine machine learning meshes network neural network surface tool transformers type

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