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 42 Citations 11,270 97 World Ranking 4112 National Ranking 379

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Convolutional neural network, Segmentation and Computer vision. Artificial intelligence is closely attributed to Pattern recognition in his study. His Deep learning research is multidisciplinary, incorporating elements of Algorithm and Data set.

His Algorithm research incorporates elements of Contextual image classification, Text mining, Medical diagnosis and Receiver operating characteristic. In his research on the topic of Convolutional neural network, Artificial neural network is strongly related with Feature extraction. His Segmentation study combines topics in areas such as Machine learning and Conditional random field.

His most cited work include:

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
  • H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (517 citations)

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

His primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Machine learning. The concepts of his Artificial intelligence study are interwoven with issues in Margin and Computer vision. Qi Dou studied Pattern recognition and Feature that intersect with Normalization.

The concepts of his Segmentation study are interwoven with issues in Leverage and Conditional random field. Qi Dou integrates many fields in his works, including Deep learning and Breast cancer. His work in Machine learning addresses issues such as Benchmark, which are connected to fields such as Relation, Medical image computing and Contextual image classification.

He most often published in these fields:

  • Artificial intelligence (95.74%)
  • Pattern recognition (45.39%)
  • Segmentation (37.59%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (95.74%)
  • Pattern recognition (45.39%)
  • Machine learning (26.95%)

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

Qi Dou mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Deep learning. His biological study spans a wide range of topics, including Margin and Computer vision. His Pattern recognition research includes themes of Feature and Transformer.

Qi Dou focuses mostly in the field of Machine learning, narrowing it down to matters related to Domain knowledge and, in some cases, Active learning and Dependency. His Deep learning research is multidisciplinary, incorporating perspectives in Ranking and Medical imaging. Qi Dou has researched Image in several fields, including Convolution and Convolutional neural network.

Between 2019 and 2021, his most popular works were:

  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (50 citations)
  • Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (41 citations)
  • Unpaired Multi-Modal Segmentation via Knowledge Distillation (33 citations)

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

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

His primary areas of study are Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Feature extraction. In his work, Task is strongly intertwined with Margin, which is a subfield of Artificial intelligence. His study in Pattern recognition concentrates on Segmentation and Normalization.

The Deep learning study combines topics in areas such as White matter and Brain segmentation. Qi Dou interconnects Brain magnetic resonance imaging and Medical imaging in the investigation of issues within Machine learning. While the research belongs to areas of Feature extraction, Qi Dou spends his time largely on the problem of Discriminative model, intersecting his research to questions surrounding Object detection, Interpolation and Object.

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

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken.
JAMA (2017)

846 Citations

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Xiaomeng Li;Hao Chen;Xiaojuan Qi;Qi Dou.
IEEE Transactions on Medical Imaging (2018)

636 Citations

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

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Qi Dou;Hao Chen;Lequan Yu;Lei Zhao.
IEEE Transactions on Medical Imaging (2016)

480 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

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

Arnaud Arindra Adiyoso Setio;Alberto Traverso;Thomas de Bel;Moira S.N. Berens.
Medical Image Analysis (2017)

428 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

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus.
arXiv: Computer Vision and Pattern Recognition (2019)

201 Citations

Best Scientists Citing Qi Dou

Pheng-Ann Heng

Pheng-Ann Heng

Chinese University of Hong Kong

Publications: 82

Nasir M. Rajpoot

Nasir M. Rajpoot

University of Warwick

Publications: 76

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 59

Lequan Yu

Lequan Yu

University of Hong Kong

Publications: 45

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 43

Bram van Ginneken

Bram van Ginneken

Radboud University Nijmegen

Publications: 41

Le Lu

Le Lu

PAII Inc.

Publications: 39

Xin Yang

Xin Yang

Sun Yat-sen University

Publications: 38

Dong Ni

Dong Ni

Shenzhen University

Publications: 38

Yefeng Zheng

Yefeng Zheng

Tencent (China)

Publications: 37

Yong Xia

Yong Xia

Northwestern Polytechnical University

Publications: 33

Daniel Rueckert

Daniel Rueckert

Technical University of Munich

Publications: 31

Nassir Navab

Nassir Navab

Technical University of Munich

Publications: 30

Ronald M. Summers

Ronald M. Summers

National Institutes of Health

Publications: 29

Jing Qin

Jing Qin

Hong Kong Polytechnic University

Publications: 28

Anant Madabhushi

Anant Madabhushi

Case Western Reserve University

Publications: 28

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.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.