Qi Dou
Impact in
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education
- Computer Vision and Pattern Recognition top 0.05%
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
-
- Medical Image Segmentation Techniques 64
- Advanced Vision and Imaging 14
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- AI in cancer detection 34
- Domain Adaptation and Few-Shot Learning 33
- Privacy-Preserving Technologies in Data 17
- Co-authors
- Pheng‐Ann Heng (99 shared papers)Hao Chen (47 shared papers)Jing Qin (41 shared papers)Lequan Yu (13 shared papers)Cheng Chen (18 shared papers)Chi‐Wing Fu (10 shared papers)Xiaojuan Qi (3 shared papers)Yueming Jin (34 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (23 papers)Medical Image Analysis (19 papers)Lecture notes in computer science (62 papers)IEEE Robotics and Automation Letters (6 papers)IEEE Journal of Biomedical and Health Informatics (5 papers)
- Partner nations
- Hong KongChinaUnited Kingdom
In The Last Decade
Qi Dou
245 papers receiving 20.2k citations
Qi Dou's Hit Papers
Peers
Comparison fields: 5 of 156
- Health Informatics 1.3k
- Computer Vision and Pattern Recognition 8k
- Artificial Intelligence 8.3k
- Radiology, Nuclear Medicine and Imaging 2.1k
- Neurology 663
Countries citing papers authored by Qi Dou
This map shows the geographic impact of Qi Dou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qi Dou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qi Dou more than expected).
Fields of papers citing papers by Qi Dou
This network shows the impact of papers produced by Qi Dou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qi Dou. The network helps show where Qi Dou may publish in the future.
Co-authors
The 25 scholars most cited alongside Qi Dou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 263 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Hit paper breakdown → | 2017 | 2514 |
| 2 | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes Hit paper breakdown → | 2018 | 1949 |
| 3 | Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge Hit paper breakdown → | 2017 | 1007 |
| 4 | Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks Hit paper breakdown → | 2016 | 908 |
| 5 | The Liver Tumor Segmentation Benchmark (LiTS) Hit paper breakdown → | 2022 | 797 |
| 6 | VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images Hit paper breakdown → | 2017 | 620 |
| 7 | Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks Hit paper breakdown → | 2016 | 544 |
| 8 | 3D deeply supervised network for automated segmentation of volumetric medical images Hit paper breakdown → | 2017 | 496 |
| 9 | Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection Hit paper breakdown → | 2016 | 487 |
| 10 | DCAN: Deep contour-aware networks for object instance segmentation from histology images Hit paper breakdown → | 2016 | 417 |
| 11 | FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space Hit paper breakdown → | 2021 | 406 |
| 12 | Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation Hit paper breakdown → | 2020 | 349 |
| 13 | 2019 | 303 | |
| 14 | 2020 | 289 | |
| 15 | 2016 | 288 | |
| 16 | 2019 | 274 | |
| 17 | 2018 | 269 | |
| 18 | 2018 | 262 | |
| 19 | 2019 | 262 | |
| 20 | Federated learning enables big data for rare cancer boundary detection Hit paper breakdown → | 2022 | 261 |
About Qi Dou
Qi Dou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Surgery and Health Informatics, having authored 263 papers that have together received 20.7k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (64 papers), Surgical Simulation and Training (40 papers), AI in cancer detection (34 papers), Domain Adaptation and Few-Shot Learning (33 papers), Robot Manipulation and Learning (28 papers), Privacy-Preserving Technologies in Data (17 papers), Advanced Vision and Imaging (14 papers) and 3D Shape Modeling and Analysis (13 papers). The work is most often cited by research in Health Informatics (1.3k citations), Computer Vision and Pattern Recognition (8k citations), Artificial Intelligence (8.3k citations), Radiology, Nuclear Medicine and Imaging (2.1k citations) and Neurology (663 citations). Qi Dou has collaborated with scholars based in Hong Kong, China and United Kingdom. Frequent co-authors include Pheng‐Ann Heng, Hao Chen, Jing Qin, Lequan Yu, Cheng Chen, Chi‐Wing Fu, Xiaojuan Qi, Yueming Jin, Xiaomeng Li and Quande Liu. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Lecture notes in computer science, IEEE Robotics and Automation Letters and IEEE Journal of Biomedical and Health Informatics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.