Pew-Thian Yap

About Pew-Thian Yap

Pew-Thian Yap, With an exceptional h-index of 53 and a recent h-index of 43 (since 2020), a distinguished researcher at University of North Carolina at Chapel Hill, specializes in the field of Magnetic Resonance Imaging, Neuroscience, Brain Development, Artificial Intelligence.

His recent articles reflect a diverse array of research interests and contributions to the field:

Automatic treatment planning for radiotherapy: a cross-modality and protocol study

Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

Learning multi-site harmonization of magnetic resonance images without traveling human phantoms

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI

Improving image segmentation with contextual and structural similarity

Federated learning for medical image analysis: A survey

Source-free unsupervised domain adaptation: A survey

A multimodal submillimeter MRI atlas of the human cerebellum

Pew-Thian Yap Information

University

Position

Associate Professor of Radiology Biomedical Engineering and Computer Science

Citations(all)

11801

Citations(since 2020)

7162

Cited By

7344

hIndex(all)

53

hIndex(since 2020)

43

i10Index(all)

182

i10Index(since 2020)

140

Email

University Profile Page

Google Scholar

Pew-Thian Yap Skills & Research Interests

Magnetic Resonance Imaging

Neuroscience

Brain Development

Artificial Intelligence

Top articles of Pew-Thian Yap

Automatic treatment planning for radiotherapy: a cross-modality and protocol study

arXiv preprint arXiv:2402.15466

2024/2/23

Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

arXiv preprint arXiv:2402.06875

2024/2/10

Learning multi-site harmonization of magnetic resonance images without traveling human phantoms

Communications Engineering

2024/1/5

Siyuan Liu
Siyuan Liu

H-Index: 4

Pew-Thian Yap
Pew-Thian Yap

H-Index: 37

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI

Patterns

2024/4/12

Improving image segmentation with contextual and structural similarity

Pattern Recognition

2024/4/9

Federated learning for medical image analysis: A survey

2024/3/12

Source-free unsupervised domain adaptation: A survey

2024/3/11

A multimodal submillimeter MRI atlas of the human cerebellum

Scientific Reports

2024/3/7

Towards Architecture-Insensitive Untrained Network Priors for Accelerated MRI Reconstruction

arXiv preprint arXiv:2312.09988

2023/12/15

Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning

arXiv preprint arXiv:2312.05986

2023/12/10

METER: Multi-task efficient transformer for no-reference image quality assessment

Applied Intelligence

2023/12

Siyuan Liu
Siyuan Liu

H-Index: 4

Pew-Thian Yap
Pew-Thian Yap

H-Index: 37

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

Cognitive Neurodynamics

2023/12

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions

Medical image analysis

2023/4/1

Organ‐aware CBCT enhancement via dual path learning for prostate cancer treatment

Medical Physics

2023/11

Attention-guided autoencoder for automated progression prediction of subjective cognitive decline with structural MRI

IEEE journal of biomedical and health informatics

2023/3/16

Improved polar complex exponential transform for robust local image description

Pattern Recognition

2023/11/1

Geng Chen
Geng Chen

H-Index: 5

Pew-Thian Yap
Pew-Thian Yap

H-Index: 37

Longitudinal prediction of postnatal brain magnetic resonance images via a metamorphic generative adversarial network

Pattern Recognition

2023/11/1

Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity

Scientific Reports

2023/3/9

Structural MRI harmonization via disentangled latent energy-based style translation

2023/10/8

See List of Professors in Pew-Thian Yap University(University of North Carolina at Chapel Hill)