Work

Modeling the Shape of the Brain Connectome via Deep Neural Networks

Year: 2023

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-031-34048-2_23

Authors: Haocheng Dai, Martin Bauer, P. Thomas Fletcher, Sarang Joshi

Institutions: University of Utah, Florida State University, University of Vienna, University of Virginia

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Science Foundation, Austrian Science Fund

Awards: Collaborative Research: Data-Driven Elastic Shape Analysis with Topological Inconsistencies and Partial Matching Constraints, CRCNS Research Proposal: Collaborative Research: The Space of Riemannian Metrics for the Statistical Analysis of the Human Connectome, G5038709275, Collaborative Research: SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging, CRCNS Research Proposal: Collaborative Research: The Space of Riemannian Metrics for the Statistical Analysis of the Human Connectome, Infinite dim. Riemannian geometry: theory and computations

OpenAlex ID: W4379660340

FWCI:

Cites: 26

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Related to: 10