Alexis V. Nees
- Pathology and Forensic Medicine top 2%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Cancer Research top 5%
- Surgery
- Co-authors
- Mark A. HelvieChintana ParamagulLubomir M. HadjiiskiHeang‐Ping ChanBerkman SahinerMarilyn A. RoubidouxCaroline E. BlaneColleen H. Neal
- Topics
- AI in cancer detection (22 papers)Radiomics and Machine Learning in Medical Imaging (13 papers)Breast Lesions and Carcinomas (12 papers)
- Partner nations
- United StatesThailandSouth Korea
In The Last Decade
Alexis V. Nees
33 papers receiving 994 citations
Peers
Comparison fields: 5 of 70
- Pathology and Forensic Medicine 465
- Artificial Intelligence 440
- Radiology, Nuclear Medicine and Imaging 401
- Cancer Research 394
- Surgery 228
Countries citing papers authored by Alexis V. Nees
This map shows the geographic impact of Alexis V. Nees'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 Alexis V. Nees with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexis V. Nees more than expected).
Fields of papers citing papers by Alexis V. Nees
This network shows the impact of papers produced by Alexis V. Nees. 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 Alexis V. Nees. The network helps show where Alexis V. Nees may publish in the future.
Co-authorship network of co-authors of Alexis V. Nees
This figure shows the co-authorship network connecting the top 25 collaborators of Alexis V. Nees. A scholar is included among the top collaborators of Alexis V. Nees 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 Alexis V. Nees. Alexis V. Nees is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 44 | |
| 3 | 21 | |
| 4 | 9 | |
| 5 | 22 | |
| 6 | 82 | |
| 7 | 30 | |
| 8 | 25 | |
| 9 | Characterization of Mammographic Masses Based on Level Set Segmentation with New Image Features and Patient Information | 1 |
| 10 | 35 | |
| 11 | 6 | |
| 12 | 78 | |
| 13 | 24 | |
| 14 | 128 | |
| 15 | 39 | |
| 16 | 29 | |
| 17 | 16 | |
| 18 | 67 | |
| 19 | 109 | |
| 20 | 52 |
About Alexis V. Nees
Alexis V. Nees is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pathology and Forensic Medicine, having authored 33 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI in cancer detection (22 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Breast Lesions and Carcinomas (12 papers). The work is most often cited by research in Cancer Research (394 citations), Pathology and Forensic Medicine (465 citations) and Radiology, Nuclear Medicine and Imaging (401 citations). Alexis V. Nees has collaborated with scholars based in United States, Thailand and South Korea. Frequent co-authors include Mark A. Helvie, Chintana Paramagul, Lubomir M. Hadjiiski, Heang‐Ping Chan, Berkman Sahiner, Marilyn A. Roubidoux, Caroline E. Blane, Colleen H. Neal, Michael S. Sabel and Lisa A. Newman. Their work appears in journals such as Radiology, Medical Physics and Annals of Surgical Oncology.
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.