Wei‐Hung Weng
- Health Informatics top 0.5%
- Artificial Intelligence top 1%
- Topic Modeling 6
- Natural Language Processing Techniques 5
- Machine Learning in Healthcare 2
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- Radiomics and Machine Learning in Medical Imaging 2
- Family Practice top 10%
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- Advanced Fluorescence Microscopy Techniques 3
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- Optical Coherence Tomography Applications 2
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- Biomedical Text Mining and Ontologies 2
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- melanin and skin pigmentation 2
- Co-authors
- Matthew B. A. McDermottJohn R. MurphyTristan NaumannEmily AlsentzerWilliam BoagPeter SzolovitsHanyi FangDi Jin
- Journals
- Cell (1 paper)Nature Communications (1 paper)SHILAP Revista de lepidopterología (1 paper)
- Partner nations
- United StatesTaiwanSingapore
In The Last Decade
Wei‐Hung Weng
24 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Health Informatics 185
- Artificial Intelligence 1.1k
- Health Information Management 120
- Radiology, Nuclear Medicine and Imaging 232
- Family Practice 21
Countries citing papers authored by Wei‐Hung Weng
This map shows the geographic impact of Wei‐Hung Weng'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 Wei‐Hung Weng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei‐Hung Weng more than expected).
Fields of papers citing papers by Wei‐Hung Weng
This network shows the impact of papers produced by Wei‐Hung Weng. 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 Wei‐Hung Weng. The network helps show where Wei‐Hung Weng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Wei‐Hung Weng, 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 | 2025 | 0 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 6 | |
| 4 | 2022 | 100 | |
| 5 | 2021 | 3 | |
| 6 | 2021 | 152 | |
| 7 | 2020 | 3 | |
| 8 | Self-Supervised Pretraining with DICOM metadata in Ultrasound Imaging | 2020 | 7 |
| 9 | 2020 | 16 | |
| 10 | 2019 | 22 | |
| 11 | Publicly Available Clinicalbreakdown → | 2019 | 781 |
| 12 | Clinically Accurate Chest X-Ray Report Generation. | 2019 | 16 |
| 13 | Park: An Open Platform for Learning-Augmented Computer Systems | 2019 | 39 |
| 14 | 2019 | 15 | |
| 15 | 2019 | 7 | |
| 16 | Unsupervised cross-modal alignment of speech and text embedding spaces | 2018 | 13 |
| 17 | 2017 | 103 | |
| 18 | 2016 | 26 | |
| 19 | 2013 | 20 | |
| 20 | 2011 | 1 |
About Wei‐Hung Weng
Wei‐Hung Weng is a scholar working on Health Informatics, Biophysics and Artificial Intelligence, having authored 25 papers that have together received 1.6k indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Advanced Fluorescence Microscopy Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Machine Learning in Healthcare (2 papers), Optical Coherence Tomography Applications (2 papers), Biomedical Text Mining and Ontologies (2 papers) and melanin and skin pigmentation (2 papers). The work is most often cited by research in Health Informatics (185 citations), Artificial Intelligence (1.1k citations) and Health Information Management (120 citations). Wei‐Hung Weng has collaborated with scholars based in United States, Taiwan and Singapore. Frequent co-authors include Matthew B. A. McDermott, John R. Murphy, Tristan Naumann, Emily Alsentzer, William Boag, Peter Szolovits, Hanyi Fang, Di Jin, Nassim Oufattole and Eileen Pan. Their work appears in journals such as Cell, Nature Communications and SHILAP Revista de lepidopterología.
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.