H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 8,199 192 World Ranking 7589 National Ranking 720

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Cancer

Jianhua Yao focuses on Artificial intelligence, Radiology, Convolutional neural network, Deep learning and Computer vision. Jianhua Yao usually deals with Artificial intelligence and limits it to topics linked to Pattern recognition and Cognitive neuroscience of visual object recognition. The study incorporates disciplines such as Computer aided detection, Colonoscopy, Virtual colonoscopy and Lung in addition to Radiology.

His study looks at the relationship between Convolutional neural network and topics such as Image, which overlap with Machine learning and Recurrent neural network. Jianhua Yao interconnects Picture archiving and communication system, Concordance, Database and Medical imaging in the investigation of issues within Deep learning. His Computer vision study integrates concerns from other disciplines, such as Computer integrated surgery and Fuzzy clustering.

His most cited work include:

  • Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2315 citations)
  • Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation (339 citations)
  • Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. (253 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision. His Artificial intelligence study combines topics in areas such as Virtual colonoscopy and Machine learning. His work deals with themes such as False positive paradox, Lung and Nuclear medicine, which intersect with Radiology.

His study ties his expertise on Contextual image classification together with the subject of Pattern recognition. His Convolutional neural network research integrates issues from Artificial neural network and Medical imaging. His studies in Computed tomography integrate themes in fields like Anatomy, Vertebra and Radiography.

He most often published in these fields:

  • Artificial intelligence (45.92%)
  • Radiology (29.86%)
  • Segmentation (23.10%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (45.92%)
  • Pattern recognition (21.97%)
  • Deep learning (7.89%)

In recent papers he was focusing on the following fields of study:

Jianhua Yao mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. The concepts of his Machine learning study are interwoven with issues in Classifier and Data set.

His Pattern recognition research incorporates themes from Breast cancer, Cytoplasm, Digital pathology and Medical imaging. Jianhua Yao interconnects Colorectal cancer and Computed tomography in the investigation of issues within Deep learning. His research integrates issues of Surgical planning and Radiology in his study of Segmentation.

Between 2017 and 2021, his most popular works were:

  • Senataxin Mutation Reveals How R-Loops Promote Transcription by Blocking DNA Methylation at Gene Promoters (60 citations)
  • Early triage of critically ill COVID-19 patients using deep learning (55 citations)
  • Convolutional Invasion and Expansion Networks for Tumor Growth Prediction (31 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Internal medicine
  • Cancer

His primary scientific interests are in Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Nuclear medicine. Jianhua Yao combines topics linked to Machine learning with his work on Artificial intelligence. His studies deal with areas such as Contextual image classification, Stain, Digital pathology and Medical imaging as well as Pattern recognition.

His Segmentation study incorporates themes from Surgical planning and Radiology. He has included themes like Cyst, Lymphangioleiomyomatosis, Lung and Abdominal ct in his Nuclear medicine study. His research investigates the connection with Artificial neural network and areas like Pascal which intersect with concerns in Training set.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu.
IEEE Transactions on Medical Imaging (2016)

2992 Citations

Polyps: Linear and Volumetric Measurement at CT Colonography

Srinath C. Yeshwant;Ronald M. Summers;Jianhua Yao;Daniel S. Brickman.
Radiology (2006)

1874 Citations

Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao.
IEEE Transactions on Medical Imaging (2016)

446 Citations

Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

Ronald M. Summers;Jianhua Yao;Perry J. Pickhardt;Marek Franaszek.
Gastroenterology (2005)

336 Citations

Danazol Treatment for Telomere Diseases.

Danielle M Townsley;Bogdan Dumitriu;Delong Liu;Angélique Biancotto.
The New England Journal of Medicine (2016)

250 Citations

Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models

Xinjian Chen;J. K. Udupa;U. Bagci;Ying Zhuge.
IEEE Transactions on Image Processing (2012)

233 Citations

Computer-integrated revision total hip replacement surgery: concept and preliminary results.

Russell H. Taylor;Leo Joskowicz;Bill Williamson;André Guéziec.
Medical Image Analysis (1999)

191 Citations

DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

Ling Zhang;Le Lu;Isabella Nogues;Ronald M. Summers.
IEEE Journal of Biomedical and Health Informatics (2017)

189 Citations

Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

Ulas Bagci;Jayaram K. Udupa;Neil Mendhiratta;Neil Mendhiratta;Brent Foster.
Medical Image Analysis (2013)

170 Citations

Automated spinal column extraction and partitioning

Jianhua Yao;S.D. O'Connor;R.M. Summers.
international symposium on biomedical imaging (2006)

166 Citations

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Best Scientists Citing Jianhua Yao

Ronald M. Summers

Ronald M. Summers

National Institutes of Health

Publications: 95

Perry J. Pickhardt

Perry J. Pickhardt

University of Wisconsin–Madison

Publications: 70

Le Lu

Le Lu

PAII Inc.

Publications: 58

Ulas Bagci

Ulas Bagci

University of Central Florida

Publications: 35

Zhengrong Liang

Zhengrong Liang

Stony Brook University

Publications: 34

Michael Fulham

Michael Fulham

University of Sydney

Publications: 33

Steve Halligan

Steve Halligan

University College London

Publications: 31

Russell H. Taylor

Russell H. Taylor

Johns Hopkins University

Publications: 30

Holger R. Roth

Holger R. Roth

Nvidia (United States)

Publications: 30

Guoyan Zheng

Guoyan Zheng

Shanghai Jiao Tong University

Publications: 30

Kenji Suzuki

Kenji Suzuki

Tokyo Institute of Technology

Publications: 27

Shuo Li

Shuo Li

University of Western Ontario

Publications: 22

Joel Moss

Joel Moss

National Institutes of Health

Publications: 21

Aly A. Farag

Aly A. Farag

University of Louisville

Publications: 21

Nassir Navab

Nassir Navab

Technical University of Munich

Publications: 20

Kensaku Mori

Kensaku Mori

Nagoya University

Publications: 20

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