An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation

arXiv:2403.06317v1 Announce Type: new
Abstract: Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model …

abstract aim ai models arxiv clinical clinical trials cost cs.ai cs.cv deep learning framework generate generative however medical medical device mesh meshes modelling samples surface synthetic type