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Zhaolin Chen

8 papers hereh-index 6152 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author5

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV3
  • cs.LG1
same name
  • Zhaolin Chen — 6 papers, h 1
  • Zhaolin Chen — 3 papers, h 3
  • Zhaolin Chen — 2 papers, h 6
  • Zhaolin Chen — 1 paper, h 1
  • Zhaolin Chen — 1 paper, h 24

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

ReDiff: Reliability-Guided Diffusion for Trustworthy Ultra-Low-Field to High-Field MRI Synthesis

Zhenxuan Zhang, Peiyuan Jing, Ruicheng Yuan +9

Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. However, in ultra-low-field settin…

cs.CV2025

Cyclic Self-Supervised Diffusion for Ultra Low-field to High-field MRI Synthesis

Zhenxuan Zhang, Peiyuan Jing, Zi Wang +12

Synthesizing high-quality images from low-field MRI holds significant potential. Low-field MRI is cheaper, more accessible, and safer, but suffers from low resolution and poor sign…

cs.CV2024

Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction

Cameron Dennis Pain, Yasmeen George, Alex Fornito +2

Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising mean…

cs.CV2024

Perivascular space Identification Nnunet for Generalised Usage (PINGU)

Benjamin Sinclair, Lucy Vivash, Jasmine Moses +16

Perivascular spaces(PVSs) form a central component of the brainś waste clearance system, the glymphatic system. These structures are visible on MRI images, and their morphology is…

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