Thomas Sanford
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Prostate Cancer Diagnosis and Treatment 20
- Prostate Cancer Treatment and Research 9
- Co-authors
- Barış TürkbeyStephanie A. HarmonBradford J. WoodPeter L. ChoykeDaguang XuZiyue XuHolger R. RothXiaosong Wang
- Journals
- Journal of Clinical Oncology (11 papers)The Journal of Urology (8 papers)Urologic Oncology Seminars and Original Investigations (3 papers)IEEE Transactions on Medical Imaging (3 papers)Academic Radiology (2 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Thomas Sanford
59 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Health Informatics 103
- Radiology, Nuclear Medicine and Imaging 583
- Computer Vision and Pattern Recognition 292
- Artificial Intelligence 420
- Pulmonary and Respiratory Medicine 390
Countries citing papers authored by Thomas Sanford
This map shows the geographic impact of Thomas Sanford'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 Thomas Sanford with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Sanford more than expected).
Fields of papers citing papers by Thomas Sanford
This network shows the impact of papers produced by Thomas Sanford. 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 Thomas Sanford. The network helps show where Thomas Sanford may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas Sanford, 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 | 2023 | 1 | |
| 2 | 2023 | 3 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 19 | |
| 5 | 2022 | 50 | |
| 6 | 2021 | 1 | |
| 7 | 2021 | 11 | |
| 8 | 2021 | 7 | |
| 9 | 2021 | 12 | |
| 10 | 2021 | 48 | |
| 11 | 2021 | 1 | |
| 12 | 2021 | 4 | |
| 13 | Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation Hit paper breakdown → | 2020 | 288 |
| 14 | 2020 | 17 | |
| 15 | 2020 | 10 | |
| 16 | 2020 | 7 | |
| 17 | 2020 | 56 | |
| 18 | 2019 | 15 | |
| 19 | 2019 | 64 | |
| 20 | 2010 | 18 |
About Thomas Sanford
Thomas Sanford is a scholar working on Health Informatics, Pulmonary and Respiratory Medicine, Urology, Cancer Research and Computer Vision and Pattern Recognition, having authored 63 papers that have together received 1.4k indexed citations. Recurring topics across this work include Prostate Cancer Diagnosis and Treatment (20 papers), Prostate Cancer Treatment and Research (9 papers), Bladder and Urothelial Cancer Treatments (9 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers), Advanced Neural Network Applications (6 papers), Cancer, Hypoxia, and Metabolism (6 papers) and Medical Image Segmentation Techniques (5 papers). The work is most often cited by research in Health Informatics (103 citations), Radiology, Nuclear Medicine and Imaging (583 citations), Computer Vision and Pattern Recognition (292 citations), Artificial Intelligence (420 citations) and Pulmonary and Respiratory Medicine (390 citations). Thomas Sanford has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Barış Türkbey, Stephanie A. Harmon, Bradford J. Wood, Peter L. Choyke, Daguang Xu, Ziyue Xu, Holger R. Roth, Xiaosong Wang, Sherif Mehralivand and Dong Yang. Their work appears in journals such as Journal of Clinical Oncology, The Journal of Urology, Urologic Oncology Seminars and Original Investigations, IEEE Transactions on Medical Imaging and Academic Radiology.
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.