arXiv:2608. 05132v1 Announce Type: cross Abstract: Predicting how a subcortical structure's shape will evolve from a few prior scans could support prognosis and clinical-trial enrichment.
arXiv:2601. 02088v3 Announce Type: replace Abstract: Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics.
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
arXiv:2606. 13188v1 Announce Type: cross Abstract: Building patient-specific cardiac models sits at the heart of precision cardiology, yet getting those models into clinical use keeps running into the same wall: mesh generation is slow, messy, and frustrating.
arXiv:2606. 08287v1 Announce Type: new Abstract: Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios.
The paper introduces an Adaptive‑Horizon Graph Neural Operator (AH‑GNO) that learns to predict bed elevation changes directly on unstructured meshes by integrating area‑weighted graph learning, historical state selection, and forecast horizon estimation. Evaluated on Telemac2D/GAIA benchmarks and a field‑scale Waiho River case, the AH‑GNO accurately reproduces key morphodynamic patterns while drastically reducing bed‑update computation time. Deployment on CPU‑GPU platforms achieves speedups of 150–360× over 4‑core GAIA CPUs and 105–186× over 20‑core CPUs.
arXiv:2610.34825v1 Announce Type: new Abstract: Diffusion MRI (dMRI) provides information about white matter microstructure that can support brain age prediction. However, many existing prediction mo...
The study investigates how to automatically predict the build orientation for selective laser melting (SLM) of dental parts using supervised machine learning. Researchers trained two different neural network backbones—ResNet‑50 on multi‑view images and PointNeXt‑S on point clouds—on about 2,400 patient‑specific parts, evaluating 13 different ways to represent the up‑axis (six classical SO(3) parameterizations and seven unit‑sphere representations). They found that applying test‑time augmentation (TTA) over 21 known rotations consistently reduced angular error, with the octahedral map achieving the lowest mean error (10.6°) on ResNet‑50, while direct S² representations performed best overall but may be influenced by label noise.
The study introduces a Riemannian shape‑space framework for analyzing age‑related morphological changes in the midsagittal corpus callosum (CC) using 128 landmarks embedded in Kendall shape space. Applied to the OASIS‑1 cohort, multivariate geodesic regression identifies anterior sub‑regions (Genu and Anterior Body) as the primary sites of age‑related deformation, outperforming Euclidean benchmarks. In Alzheimer’s patients, age explains less than 0.5% of shape variance, and a tangent‑space classifier achieves an age‑group AUC of 0.791 from 2D contour data alone.
arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.
NeuralSRNF is a new framework that enables efficient statistical shape analysis and generation of genus‑zero 3D and 4D objects undergoing nonrigid deformations. It replaces costly numerical SRNF inversion with a continuous, resolution‑agnostic neural representation that accurately reconstructs shapes and computes inverse SRNF maps in under 3 s, compared to over 10 min for traditional methods. Experiments on multiple datasets show that NeuralSRNF outperforms existing techniques in accuracy and speed across tasks such as geodesic computation, deformation transfer, statistical summarization, and shape generation.
BrainDyn is a graph ordinary differential equation (ODE) network that models brain activity as coordinated patterns across connected regions. It uses a cellular sheaf with dynamic restriction maps to mediate region-to-region communication through learned linear transformations, rather than the standard shared feature space and scalar summation of message‑passing networks. Evaluated on resting‑state fMRI, EEG, and simulated NEST data, BrainDyn outperforms competing models in multi‑region forecasting, autoregressive generation of brain transients, and prediction of altered transients after controlled perturbations, while its learned features are expressive, interpretable, and biologically aligned.
arXiv:2607. 11974v1 Announce Type: cross Abstract: Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen.