Yi‐Ta Wu
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Pulmonary and Respiratory Medicine
- Signal Processing
- Co-authors
- Frank Y. ShihJiazheng ShiBerkman SahinerLubomir M. HadjiiskiJun WeiHeang‐Ping ChanChuan ZhouMark A. Helvie
- Topics
- AI in cancer detection (6 papers)Advanced Steganography and Watermarking Techniques (6 papers)Advanced Image and Video Retrieval Techniques (6 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Partner nations
- United StatesTaiwan
In The Last Decade
Yi‐Ta Wu
26 papers receiving 475 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 342
- Artificial Intelligence 174
- Radiology, Nuclear Medicine and Imaging 124
- Pulmonary and Respiratory Medicine 68
- Signal Processing 42
Countries citing papers authored by Yi‐Ta Wu
This map shows the geographic impact of Yi‐Ta Wu'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 Yi‐Ta Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi‐Ta Wu more than expected).
Fields of papers citing papers by Yi‐Ta Wu
This network shows the impact of papers produced by Yi‐Ta Wu. 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 Yi‐Ta Wu. The network helps show where Yi‐Ta Wu may publish in the future.
Co-authorship network of co-authors of Yi‐Ta Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Yi‐Ta Wu. A scholar is included among the top collaborators of Yi‐Ta Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yi‐Ta Wu. Yi‐Ta Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 22 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 23 | |
| 7 | 12 | |
| 8 | 26 | |
| 9 | 1 | |
| 10 | 78 | |
| 11 | 18 | |
| 12 | 41 | |
| 13 | 12 | |
| 14 | 24 | |
| 15 | 21 | |
| 16 | 31 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | 13 | |
| 20 | 56 |
About Yi‐Ta Wu
Yi‐Ta Wu is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 29 papers that have together received 519 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Advanced Steganography and Watermarking Techniques (6 papers) and Advanced Image and Video Retrieval Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (342 citations), Artificial Intelligence (174 citations) and Radiology, Nuclear Medicine and Imaging (124 citations). Yi‐Ta Wu has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Frank Y. Shih, Jiazheng Shi, Berkman Sahiner, Lubomir M. Hadjiiski, Jun Wei, Heang‐Ping Chan, Chuan Zhou, Mark A. Helvie, James Geller and Soon Ae Chun. Their work appears in journals such as Pattern Recognition, Medical Physics and Computer Vision and Image Understanding.
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.