Hao Chen
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Advanced Image and Video Retrieval Techniques
- Digital Imaging for Blood Diseases
- Health Informatics top 0.1%
Papers in
-
- Advanced Neural Network Applications 58
- Medical Image Segmentation Techniques 35
- Advanced Image and Video Retrieval Techniques 32
-
- Radiomics and Machine Learning in Medical Imaging 62
- COVID-19 diagnosis using AI 26
- Retinal Imaging and Analysis 24
- Co-authors
- Pheng‐Ann Heng (83 shared papers)Chunhua Shen (15 shared papers)Zhi Tian (7 shared papers)Qi Dou (48 shared papers)Tong He (3 shared papers)Lequan Yu (19 shared papers)Jing Qin (25 shared papers)Xiaojuan Qi (5 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (37 papers)Medical Image Analysis (24 papers)Nature Communications (10 papers)IEEE Journal of Biomedical and Health Informatics (9 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (6 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Hao Chen
420 papers receiving 30.9k citations
Hao Chen's Hit Papers
Peers
Comparison fields: 5 of 215
- Computer Vision and Pattern Recognition 14.1k
- Health Informatics 783
- Radiology, Nuclear Medicine and Imaging 9.7k
- Artificial Intelligence 11.7k
- Neurology 1.9k
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 465 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 | 4695 |
| 2 | Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Hit paper breakdown → | 2017 | 2461 |
| 3 | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes Hit paper breakdown → | 2018 | 1913 |
| 4 | Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge Hit paper breakdown → | 2017 | 988 |
| 5 | Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks Hit paper breakdown → | 2016 | 896 |
| 6 | Gland segmentation in colon histology images: The glas challenge contest Hit paper breakdown → | 2016 | 754 |
| 7 | FCOS: A Simple and Strong Anchor-free Object Detector Hit paper breakdown → | 2020 | 677 |
| 8 | The Liver Tumor Segmentation Benchmark (LiTS) Hit paper breakdown → | 2022 | 664 |
| 9 | VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images Hit paper breakdown → | 2017 | 604 |
| 10 | Conditional Convolutions for Instance Segmentation Hit paper breakdown → | 2020 | 534 |
| 11 | Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks Hit paper breakdown → | 2016 | 533 |
| 12 | BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation Hit paper breakdown → | 2020 | 487 |
| 13 | 3D deeply supervised network for automated segmentation of volumetric medical images Hit paper breakdown → | 2017 | 485 |
| 14 | Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection Hit paper breakdown → | 2016 | 484 |
| 15 | DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation Hit paper breakdown → | 2016 | 426 |
| 16 | Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation Hit paper breakdown → | 2020 | 421 |
| 17 | DCAN: Deep contour-aware networks for object instance segmentation from histology images Hit paper breakdown → | 2016 | 412 |
| 18 | From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge Hit paper breakdown → | 2018 | 406 |
| 19 | A Multi-Organ Nucleus Segmentation Challenge Hit paper breakdown → | 2019 | 390 |
| 20 | 2017 | 353 |
About Hao Chen
Hao Chen is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Ophthalmology and Media Technology, having authored 465 papers that have together received 31.7k indexed citations. Recurring topics across this work include AI in cancer detection (75 papers), Radiomics and Machine Learning in Medical Imaging (62 papers), Advanced Neural Network Applications (58 papers), Medical Image Segmentation Techniques (35 papers), Domain Adaptation and Few-Shot Learning (33 papers), Advanced Image and Video Retrieval Techniques (32 papers), COVID-19 diagnosis using AI (26 papers) and Retinal Imaging and Analysis (24 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (14.1k citations), Health Informatics (783 citations), Radiology, Nuclear Medicine and Imaging (9.7k citations), Artificial Intelligence (11.7k citations) and Neurology (1.9k 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, Lequan Yu, Jing Qin, Xiaojuan Qi, Chi‐Wing Fu and Xiaomeng Li. Their work appears in journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Nature Communications, IEEE Journal of Biomedical and Health Informatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.