11 citations · 24 across the 7 of their papers we have counts for
8 papers
NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI
Felix Nieto-del-Amor, Jingru Fu, J. -Sebastian Muehlboeck +3
Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, and treatment planning; howeve…
: learning to disentangle technical distortions from true biological change
Jingru Fu, Kathleen E. Larson, Douglas N. Greve +2
Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registration is a key tool for estimati…
Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport
Jingru Fu, Yuqi Zheng, Neel Dey +2
Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or di…
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
Jingru Fu, Adrian V. Dalca, Bruce Fischl +2
Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-…
Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
Jingru Fu, Simone Bendazzoli, Örjan Smedby +1
Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when applied t…
A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates
Jingru Fu, Daniel Ferreira, Örjan Smedby +1
Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration p…
Generative Aging of Brain Images with Diffeomorphic Registration
Jingru Fu, Antonios Tzortzakakis, José Barroso +3
Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. The technique of neuroimaging, such as Magnetic Resonance Imagin…
Adversarial Transfer Learning for Cross-domain Visual Recognition
Shanshan Wang, Lei Zhang, JingRu Fu
In many practical visual recognition scenarios, feature distribution in the source domain is generally different from that of the target domain, which results in the emergence of g…