Hao Chen
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Advanced Image and Video Retrieval Techniques
- Health Informatics top 0.2%
Papers in
-
- Advanced Neural Network Applications 40
- Medical Image Segmentation Techniques 27
- Advanced Image and Video Retrieval Techniques 26
-
- AI in cancer detection 51
- Domain Adaptation and Few-Shot Learning 18
- Co-authors
- Pheng‐Ann Heng (56 shared papers)Chunhua Shen (12 shared papers)Zhi Tian (6 shared papers)Qi Dou (31 shared papers)Tong He (4 shared papers)Jing Qin (18 shared papers)Lequan Yu (16 shared papers)Xiaojuan Qi (5 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (31 papers)Medical Image Analysis (19 papers)IEEE Journal of Biomedical and Health Informatics (8 papers)Nature Communications (6 papers)Frontiers in Oncology (5 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Hao Chen
315 papers receiving 18.9k citations
Hao Chen's Hit Papers
Peers
Comparison fields: 5 of 211
- Computer Vision and Pattern Recognition 9.7k
- Health Informatics 345
- Radiology, Nuclear Medicine and Imaging 5.9k
- Artificial Intelligence 6.8k
- Neurology 1.4k
Countries citing papers authored by Hao Chen
This map shows the geographic impact of Hao Chen'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 Hao Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hao Chen more than expected).
Fields of papers citing papers by Hao Chen
This network shows the impact of papers produced by Hao Chen. 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 Hao Chen. The network helps show where Hao Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Hao Chen, 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 354 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | FCOS: Fully Convolutional One-Stage Object Detection Hit paper breakdown → | 2019 | 3976 |
| 2 | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes Hit paper breakdown → | 2018 | 1661 |
| 3 | Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks Hit paper breakdown → | 2016 | 775 |
| 4 | FCOS: A Simple and Strong Anchor-free Object Detector Hit paper breakdown → | 2020 | 580 |
| 5 | Gland segmentation in colon histology images: The glas challenge contest Hit paper breakdown → | 2016 | 570 |
| 6 | VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images Hit paper breakdown → | 2017 | 506 |
| 7 | Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks Hit paper breakdown → | 2016 | 471 |
| 8 | BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation Hit paper breakdown → | 2020 | 431 |
| 9 | Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection Hit paper breakdown → | 2016 | 423 |
| 10 | 3D deeply supervised network for automated segmentation of volumetric medical images Hit paper breakdown → | 2017 | 418 |
| 11 | DCAN: Deep contour-aware networks for object instance segmentation from histology images Hit paper breakdown → | 2016 | 351 |
| 12 | Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation Hit paper breakdown → | 2020 | 343 |
| 13 | DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation Hit paper breakdown → | 2016 | 328 |
| 14 | 2015 | 265 | |
| 15 | 2019 | 264 | |
| 16 | Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation Hit paper breakdown → | 2020 | 261 |
| 17 | 2018 | 247 | |
| 18 | 2017 | 238 | |
| 19 | Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma Hit paper breakdown → | 2019 | 237 |
| 20 | 2018 | 217 |
About Hao Chen
Hao Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Surgery and Biomedical Engineering, having authored 354 papers that have together received 19.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (54 papers), AI in cancer detection (51 papers), Advanced Neural Network Applications (40 papers), Medical Image Segmentation Techniques (27 papers), Advanced Image and Video Retrieval Techniques (26 papers), Retinal Imaging and Analysis (21 papers), Domain Adaptation and Few-Shot Learning (18 papers) and Medical Imaging and Analysis (16 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (9.7k citations), Health Informatics (345 citations), Radiology, Nuclear Medicine and Imaging (5.9k citations), Artificial Intelligence (6.8k citations) and Neurology (1.4k citations). Hao Chen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Pheng‐Ann Heng, Chunhua Shen, Zhi Tian, Qi Dou, Tong He, Jing Qin, Lequan Yu, Xiaojuan Qi, Chi‐Wing Fu and Xiaomeng Li. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, IEEE Journal of Biomedical and Health Informatics, Nature Communications and Frontiers in Oncology.
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.