arXiv:2606. 17022v1 Announce Type: cross Abstract: A central objective of machine learning is to identify structure and patterns in data.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2602. 11467v2 Announce Type: replace Abstract: Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research.
arXiv:2608. 02306v1 Announce Type: cross Abstract: We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain.
We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain. This Push-Forward Transform enables invariant and robust comparison of shapes, preserving intrinsic geometric information.
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.
arXiv:2606. 17836v1 Announce Type: cross Abstract: Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis.
arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.