William T. Katz
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Biophysics top 10%
- Structural Biology top 10%
- Cognitive Neuroscience
- Co-authors
- Michael MerickelJohn W. SnellStephen M. PlazaRyan KennedyJuan Nunez-IglesiasJacquelyn BlackstoneMichael G. PinetteAngelina Cartin
- Topics
- Medical Image Segmentation Techniques (7 papers)Neural Networks and Applications (3 papers)AI in cancer detection (3 papers)
- Journals
- Methods in enzymology on CD-ROM/Methods in enzymologyAmerican Journal of Obstetrics and GynecologyFrontiers in Neural Circuits
- Partner nations
- United StatesAustraliaItaly
In The Last Decade
William T. Katz
13 papers receiving 125 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 29
- Computer Vision and Pattern Recognition 27
- Biophysics 27
- Structural Biology 18
- Cognitive Neuroscience 16
Countries citing papers authored by William T. Katz
This map shows the geographic impact of William T. Katz'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 William T. Katz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William T. Katz more than expected).
Fields of papers citing papers by William T. Katz
This network shows the impact of papers produced by William T. Katz. 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 William T. Katz. The network helps show where William T. Katz may publish in the future.
Co-authorship network of co-authors of William T. Katz
This figure shows the co-authorship network connecting the top 25 collaborators of William T. Katz. A scholar is included among the top collaborators of William T. Katz 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 William T. Katz. William T. Katz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 13 | |
| 3 | 29 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 13 | |
| 8 | 1 | |
| 9 | Semiautomatic Model-Based Segmentation of the Brain from Magnetic Resonance Images | 1 |
| 10 | 3 | |
| 11 | 53 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 10 |
About William T. Katz
William T. Katz is a scholar working on Structural Biology, Computer Vision and Pattern Recognition and Biophysics, having authored 15 papers that have together received 134 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers), Neural Networks and Applications (3 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Structural Biology (18 citations), Biophysics (27 citations) and Obstetrics and Gynecology (13 citations). William T. Katz has collaborated with scholars based in United States, Australia and Italy. Frequent co-authors include Michael Merickel, John W. Snell, Stephen M. Plaza, Ryan Kennedy, Juan Nunez-Iglesias, Jacquelyn Blackstone, Michael G. Pinette, Angelina Cartin, John C. Goble and John Snell. Their work appears in journals such as Methods in enzymology on CD-ROM/Methods in enzymology, American Journal of Obstetrics and Gynecology and Frontiers in Neural Circuits.
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.