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Jun Li

Institute of Computing Technology, Chinese Academy of Sciences

4 papers hereh-index 9694 citations16 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • eess.IV3
  • cs.CV1
affiliations
  • Institute of Computing Technology, Chinese Academy of Sciences
ORCID 0000-0001-8859-9377
same name
  • Jun Li — 17 papers, h 4
  • Jun Li — 14 papers, h 17
  • Jun Li — 11 papers, h 5
  • Jun Li — 9 papers, h 13
  • Jun Li — 8 papers, h 4
  • Jun Li — 8 papers, h 4

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

collaborators

4 papers

eess.IV2024

CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

Yang Deng, Ce Wang, Yuan Hui +12

Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine imaging is an essential tool for noninvasively visualizing and assessing spinal pathology.…

eess.IV2024

3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation

Xueming Fu, Yingtai Li, Fenghe Tang +4

Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often req…

cs.CV2024

Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review

Zikang Xu, Jun Li, Qingsong Yao +3

Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity i…

eess.IV2024

Which images to label for few-shot medical landmark detection?

Quan Quan, Qingsong Yao, Jun Li +1

The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requ…

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