Hyeonseob Nam
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Health Informatics top 1%
- Pulmonary and Respiratory Medicine
- Topics
- Radiomics and Machine Learning in Medical Imaging (5 papers)AI in cancer detection (4 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Partner nations
- South Korea
In The Last Decade
Hyeonseob Nam
8 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 649
- Computer Vision and Pattern Recognition 590
- Radiology, Nuclear Medicine and Imaging 281
- Health Informatics 108
- Pulmonary and Respiratory Medicine 84
Countries citing papers authored by Hyeonseob Nam
This map shows the geographic impact of Hyeonseob Nam'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 Hyeonseob Nam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyeonseob Nam more than expected).
Fields of papers citing papers by Hyeonseob Nam
This network shows the impact of papers produced by Hyeonseob Nam. 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 Hyeonseob Nam. The network helps show where Hyeonseob Nam may publish in the future.
Co-authorship network of co-authors of Hyeonseob Nam
This figure shows the co-authorship network connecting the top 25 collaborators of Hyeonseob Nam. A scholar is included among the top collaborators of Hyeonseob Nam 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 Hyeonseob Nam. Hyeonseob Nam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 21 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader studybreakdown → | 309 |
| 6 | 192 | |
| 7 | Reducing Domain Gap via Style-Agnostic Networks. | 14 |
| 8 | Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks | 34 |
| 9 | Dual Attention Networks for Multimodal Reasoning and Matchingbreakdown → | 497 |
About Hyeonseob Nam
Hyeonseob Nam is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (4 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Health Informatics (108 citations), Computer Vision and Pattern Recognition (590 citations) and Artificial Intelligence (649 citations). Hyeonseob Nam has collaborated with scholars based in South Korea. Frequent co-authors include Jung-Woo Ha, Jeonghee Kim, Hyoeun Kim, Hyunjae Lee, Kyunghwa Han, Eun Hye Lee, Kihwan Kim, Hak Hee Kim, Boo‐Kyung Han and Eun‐Kyung Kim. Their work appears in journals such as Journal of Clinical Oncology, The Lancet Digital Health and Radiology Artificial Intelligence.
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.