Search Results for author:
Found 8 papers, 2 papers with code
Learning Geodesics of Geometric Shape Deformations From Images
In particular, the capability of our proposed GDN being able to predict geodesics is important for quantifying and comparing deformable shape presented in images.
TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration
This paper presents a novel approach, termed {\em Temporal Latent Residual Network (TLRN)}, to predict a sequence of deformation fields in time-series image registration.
LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation
More specifically, our method first employs an encoder from a pre-trained registration network that learns latent motion features (also considered as deformation-based shape features) from image sequences.
Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images.
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces
To achieve this, we develop a neural operator that for the first time learns the evolving trajectory of geodesic deformations parameterized in the tangent space of diffeomorphisms(a. k. a velocity fields).
From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
Here, an MD dataset containing 3, 218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories.
Hybrid Atlas Building with Deep Registration Priors
Registration-based atlas building often poses computational challenges in high-dimensional image spaces.
MS$^2$-Transformer: An End-to-End Model for MS/MS-assisted Molecule Identification
Manually interpreting the MS/MS spectrum into the molecules (i. e., the simplified molecular-input line-entry system, SMILES) is often costly and cumbersome, mainly due to the synthesis and labeling of isotopes and the requirement of expert knowledge.
