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Found 7 papers, 2 papers with code
Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning
The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise.
CryoChains: Heterogeneous Reconstruction of Molecular Assembly of Semi-flexible Chains from Cryo-EM Images
Cryogenic electron microscopy (cryo-EM) has transformed structural biology by allowing to reconstruct 3D biomolecular structures up to near-atomic resolution.
3D Generative Model Latent Disentanglement via Local Eigenprojection
Designing realistic digital humans is extremely complex.
Heterogeneous reconstruction of deformable atomic models in Cryo-EM
Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules.
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces
Learning a disentangled, interpretable, and structured latent representation in 3D generative models of faces and bodies is still an open problem.
Intraoperative Liver Surface Completion with Graph Convolutional VAE
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure.
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications.
