D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 38 Citations 6,703 152 World Ranking 5043 National Ranking 474

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Convolutional neural network, Segmentation and Deep learning. His biological study spans a wide range of topics, including Machine learning, Residual and Computer vision. The concepts of his Pattern recognition study are interwoven with issues in Object detection, Shadow, Reduction and Feature.

When carried out as part of a general Segmentation research project, his work on Image segmentation and Scale-space segmentation is frequently linked to work in Process, therefore connecting diverse disciplines of study. His study in Deep learning is interdisciplinary in nature, drawing from both Cross-validation, Leverage, Elastic net regularization, Feature learning and Semantic feature. His Feature extraction research is multidisciplinary, incorporating elements of Recurrent neural network and Discriminative model.

His most cited work include:

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (409 citations)
  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (349 citations)
  • 3D deeply supervised network for automated segmentation of volumetric medical images. (289 citations)

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

Jing Qin mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning. His Artificial intelligence study frequently links to related topics such as Machine learning. His research in Pattern recognition focuses on subjects like Artificial neural network, which are connected to Medical imaging.

His Segmentation research includes elements of Object, Convolution and Representation. Jing Qin incorporates Deep learning and Multi-task learning in his research. His studies in Convolutional neural network integrate themes in fields like Transfer of learning, Object detection and Leverage.

He most often published in these fields:

  • Artificial intelligence (77.16%)
  • Pattern recognition (48.73%)
  • Segmentation (26.40%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (77.16%)
  • Pattern recognition (48.73%)
  • Segmentation (26.40%)

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

Artificial intelligence, Pattern recognition, Segmentation, Convolutional neural network and Discriminative model are his primary areas of study. His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His study on Feature extraction is often connected to Traffic flow as part of broader study in Pattern recognition.

Jing Qin has researched Segmentation in several fields, including Semantics, Net and Retinal vessel. His Discriminative model research includes themes of Nonlinear dimensionality reduction, Norm, Adaptive control and Embedding. His Deep learning study combines topics from a wide range of disciplines, such as Nuclear medicine and Radiography.

Between 2020 and 2021, his most popular works were:

  • Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module (4 citations)
  • A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting (3 citations)
  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space. (1 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Image segmentation and Discriminative model. His Benchmark, Segmentation and Data set study in the realm of Artificial intelligence connects with subjects such as Mechanism and Code. The Benchmark study combines topics in areas such as Recurrent neural network, Noise and Outlier.

His research integrates issues of Weighting, Feature extraction and Deep learning in his study of Segmentation. His Data set research incorporates elements of Similarity, Norm, Adaptive control and Feature selection. Jing Qin undertakes interdisciplinary study in the fields of Context model and Convolutional neural network through his works.

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

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Lequan Yu;Hao Chen;Qi Dou;Jing Qin.
IEEE Transactions on Medical Imaging (2017)

500 Citations

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

Hao Chen;Qi Dou;Lequan Yu;Jing Qin.
NeuroImage (2017)

430 Citations

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

Qi Dou;Hao Chen;Lequan Yu;Jing Qin.
IEEE Transactions on Biomedical Engineering (2017)

363 Citations

3D deeply supervised network for automated segmentation of volumetric medical images.

Qi Dou;Lequan Yu;Hao Chen;Yueming Jin.
Medical Image Analysis (2017)

328 Citations

DCAN: Deep contour-aware networks for object instance segmentation from histology images

Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou.
Medical Image Analysis (2017)

268 Citations

R³Net: Recurrent Residual Refinement Network for Saliency Detection

Zijun Deng;Xiaowei Hu;Lei Zhu;Lei Zhu;Xuemiao Xu.
international joint conference on artificial intelligence (2018)

246 Citations

Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images.

Lequan Yu;Xin Yang;Hao Chen;Jing Qin.
national conference on artificial intelligence (2017)

236 Citations

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

Qi Dou;Hao Chen;Yueming Jin;Lequan Yu.
medical image computing and computer assisted intervention (2016)

174 Citations

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos

Lequan Yu;Hao Chen;Qi Dou;Jing Qin.
IEEE Journal of Biomedical and Health Informatics (2017)

121 Citations

Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

Cheng Chen;Qi Dou;Hao Chen;Jing Qin.
national conference on artificial intelligence (2019)

118 Citations

Best Scientists Citing Jing Qin

Pheng-Ann Heng

Pheng-Ann Heng

Chinese University of Hong Kong

Publications: 127

Qi Dou

Qi Dou

Chinese University of Hong Kong

Publications: 59

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 54

Dong Ni

Dong Ni

Shenzhen University

Publications: 52

Xin Yang

Xin Yang

Sun Yat-sen University

Publications: 48

Lequan Yu

Lequan Yu

University of Hong Kong

Publications: 36

Tianfu Wang

Tianfu Wang

Shenzhen University

Publications: 36

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 30

Chi-Wing Fu

Chi-Wing Fu

Chinese University of Hong Kong

Publications: 29

Hao Chen

Hao Chen

Chinese University of Hong Kong

Publications: 28

Yong Xia

Yong Xia

Northwestern Polytechnical University

Publications: 28

Huchuan Lu

Huchuan Lu

Dalian University of Technology

Publications: 27

Daniel Rueckert

Daniel Rueckert

Technical University of Munich

Publications: 26

Nasir M. Rajpoot

Nasir M. Rajpoot

University of Warwick

Publications: 25

Yefeng Zheng

Yefeng Zheng

Tencent (China)

Publications: 22

Huazhu Fu

Huazhu Fu

Agency for Science, Technology and Research

Publications: 19

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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