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Computer Science

D-Index
82
Citations
22026
World Ranking
980
National Ranking
526

Overview

Heang Ping Chan is affiliated with the University of Michigan-Ann Arbor in the United States. Their research spans multiple fields, primarily focusing on Medicine and Computer Science. Within these broad areas, they work extensively in subfields including Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Biomedical Engineering, and Health Informatics.

Their scientific contributions address numerous topics, particularly in medical imaging and artificial intelligence. Key subjects in their work are:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Advanced X-ray and CT Imaging
  • Digital Radiography and Breast Imaging
  • Artificial Intelligence in Healthcare and Education
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment

Heang Ping Chan has published research in several prominent venues, including the following frequent publication sites:

  • Medical Physics
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • Tomography
  • BJR|Artificial Intelligence
  • Cancers

Among their recent papers are notable works such as:

  • "Deep Learning in Medical Image Analysis," 2020, published in Advances in Experimental Medicine and Biology
  • "Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI," 2022, published in Nature Medicine
  • "Computer-aided diagnosis in the era of deep learning," 2020, published in Medical Physics
  • "AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging," 2022, published in Medical Physics
  • "Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets," 2020, published in Tomography

Their collaborative work involves coauthors frequently engaged in related research areas. Frequent coauthors include:

  • Lubomir M. Hadjiiski
  • Ravi K. Samala
  • Chuan Zhou
  • Mark A. Helvie
  • Richard H. Cohan

Heang Ping Chan's body of work has a significant focus on artificial intelligence applications in medical imaging, addressing issues in cancer detection, imaging techniques, and clinical decision support systems. The combination of their expertise in both medicine and computer science allows for interdisciplinary approaches to advancing medical technologies and diagnostic accuracy.

Best Publications

  • Deep Learning in Medical Image Analysis.

    Heang-Ping Chan;Ravi K. Samala;Lubomir M. Hadjiiski;Chuan Zhou

  • Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography

    Heang Ping Chan;Kunio Doi;Simranjit Galhotra;Carl J. Vyborny

  • Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

    B. Sahiner;Heang-Ping Chan;N. Petrick;Datong Wei

  • Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.

    H P Chan;K Doi;C J Vyborny;R A Schmidt

  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

    Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan

  • Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

    Maryellen L. Giger;Heang Ping Chan;John M Boone

  • Artificial convolution neural network for medical image pattern recognition

    Shih-Chung B. Lo;Heang-Ping Chan;Jyh-Shyan Lin;Huai Li

  • A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis

    Yiheng Zhang;Heang Ping Chan;Berkman Sahiner;Jun Wei

  • Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

    Heang-Ping Chan;Berkman Sahiner;Mark A. Helvie;Nicholas Petrick

  • Computer-aided diagnosis in the era of deep learning.

    Heang-Ping Chan;Lubomir M Hadjiiski;Ravi K Samala

  • An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

    N. Petrick;Heang-Ping Chan;B. Sahiner;Datong Wei

  • Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

    Heang-Ping Chan;Datong Wei;Mark A. Helvie;Berkman Sahiner

  • Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

    Heang Ping Chan;Berkman Sahiner;Kwok Leung Lam;Nicholas Petrick

  • Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

    Ted W. Way;Lubomir M. Hadjiiski;Berkman Sahiner;Heang Ping Chan

  • Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

    Berkman Sahiner;Heang Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network

    Heang Ping Chan;Shih Chung B. Lo;Berkman Sahiner;Kwok Leung Lam

  • Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study.

    Heang Ping Chan;Kunio Doi;Carl J. Vyborny;Kwok Leung Lam

  • Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

    Ravi K. Samala;Heang Ping Chan;Lubomir Hadjiiski;Mark A. Helvie

  • Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

    Kenny H. Cha;Lubomir Hadjiiski;Ravi K. Samala;Heang Ping Chan

  • Computerized image analysis: estimation of breast density on mammograms.

    Chuan Zhou;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

  • Improvement of mammographic mass characterization using spiculation measures and morphological features

    Berkman Sahiner;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie

Frequent Co-Authors

Lubomir M. Hadjiiski
Lubomir M. Hadjiiski University of Michigan–Ann Arbor
Berkman Sahiner
Berkman Sahiner United States Food and Drug Administration
Nicholas Petrick
Nicholas Petrick US Food and Drug Administration
Kunio Doi
Kunio Doi University of Chicago
Charles E. Metz
Charles E. Metz University of Chicago
Paul L. Carson
Paul L. Carson University of Michigan–Ann Arbor
Metin N. Gurcan
Metin N. Gurcan Wake Forest University
Jeffrey A. Fessler
Jeffrey A. Fessler University of Michigan–Ann Arbor
Hiroshi Fujita
Hiroshi Fujita Gifu University
Ronald M. Summers
Ronald M. Summers National Institutes of Health

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