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Pingkun Yan

Rensselaer Polytechnic Institute

18 papers hereh-index 479.8k citations186 works total

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

author position
  • first author2
  • middle author5
  • last author10

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV10
  • eess.IV3
  • physics.med-ph3
  • cs.CL1
  • q-bio.QM1
affiliations
  • Rensselaer Polytechnic Institute
Homepage
same name
  • Pingkun Yan — 17 papers, h 21
  • Pingkun Yan — 4 papers, h 3
  • Pingkun Yan — 1 paper

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
20172023
most citedCT Image Denoising with Perceptive Deep Neural Networks

57 citations · 101 across the 7 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018

Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images

Pingkun Yan, Hengtao Guo, Ge Wang +2

Known for its high morbidity and mortality rates, lung cancer poses a significant threat to human health and well-being. However, the same population is also at high risk for other…

physics.med-ph2018

Multifactorial cancer treatment outcome prediction through multifaceted radiomics

Zhiguo Zhou, David Sher, Qiongwen Zhang +6

Accurately predicting the treatment outcome plays a greatly important role in tailoring and adapting a treatment planning in cancer therapy. Although the development of different m…

cs.CV2018

Learning Deep Similarity Metric for 3D MR-TRUS Registration

Grant Haskins, Jochen Kruecker, Uwe Kruger +4

Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressi…

cs.CV2018

Adversarial Image Registration with Application for MR and TRUS Image Fusion

Pingkun Yan, Sheng Xu, Ardeshir R. Rastinehad +1

Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.