Haoyu Dong
- Health Informatics top 10%
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- Medical Image Segmentation Techniques 2
- Generative Adversarial Networks and Image Synthesis 1
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- MRI in cancer diagnosis 1
- Artificial Intelligence top 10%
- AI in cancer detection 5
- Anomaly Detection Techniques and Applications 2
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- Digital Radiography and Breast Imaging 2
- Co-authors
- Maciej A. MazurowskiHanxue GuNicholas KonzJichen YangYixin ZhangYuansi ChenE. Shelley HwangAllan D. Kirk
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- Scientific Reports (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)IEEE Transactions on Medical Imaging (2 papers)
- Partner nations
- United States
In The Last Decade
Haoyu Dong
6 papers receiving 352 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Health Informatics 14
- Computer Vision and Pattern Recognition 139
- Radiology, Nuclear Medicine and Imaging 128
- Neurology 38
- Artificial Intelligence 100
Countries citing papers authored by Haoyu Dong
This map shows the geographic impact of Haoyu Dong'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 Haoyu Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haoyu Dong more than expected).
Fields of papers citing papers by Haoyu Dong
This network shows the impact of papers produced by Haoyu Dong. 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 Haoyu Dong. The network helps show where Haoyu Dong may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Haoyu Dong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2026 | 0 | |
| 2 | 2025 | 5 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 7 | |
| 10 | Segment anything model for medical image analysis: An experimental studybreakdown → | 2023 | 342 |
About Haoyu Dong
Haoyu Dong is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 361 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Medical Image Segmentation Techniques (2 papers), COVID-19 diagnosis using AI (2 papers), Anomaly Detection Techniques and Applications (2 papers), Digital Radiography and Breast Imaging (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), MRI in cancer diagnosis (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Health Informatics (14 citations), Computer Vision and Pattern Recognition (139 citations) and Radiology, Nuclear Medicine and Imaging (128 citations). Haoyu Dong has collaborated with scholars based in United States. Frequent co-authors include Maciej A. Mazurowski, Hanxue Gu, Nicholas Konz, Jichen Yang, Yixin Zhang, Yuansi Chen, E. Shelley Hwang, Allan D. Kirk, Yaqian Chen and Majid Harouni. Their work appears in journals such as Scientific Reports, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging.
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.