Gi-Tae Han
Impact in
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Non-Invasive Vital Sign Monitoring 4
- Advanced Chemical Sensor Technologies 3
- Co-authors
- Zong Woo Geem (5 shared papers)Ram Sarkar (2 shared papers)Rohit Kundu (1 shared paper)Seong-Hoon Kim (5 shared papers)Arpan Basu (1 shared paper)Jin‐Woo Hong (1 shared paper)
- Journals
- Sensors (3 papers)PLoS ONE (1 paper)Swarm and Evolutionary Computation (1 paper)Diagnostics (1 paper)KIPS Transactions on Software and Data Engineering (2 papers)
- Partner nations
- South KoreaIndia
In The Last Decade
Gi-Tae Han
8 papers receiving 345 citations
Gi-Tae Han's Hit Papers
Peers
Comparison fields: 5 of 82
- Radiology, Nuclear Medicine and Imaging 204
- Health Informatics 10
- Health Information Management 23
- Artificial Intelligence 150
- Computer Vision and Pattern Recognition 60
Countries citing papers authored by Gi-Tae Han
This map shows the geographic impact of Gi-Tae Han'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 Gi-Tae Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gi-Tae Han more than expected).
Fields of papers citing papers by Gi-Tae Han
This network shows the impact of papers produced by Gi-Tae Han. 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 Gi-Tae Han. The network helps show where Gi-Tae Han may publish in the future.
Co-authors
The 6 scholars most cited alongside Gi-Tae Han, 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 | Pneumonia detection in chest X-ray images using an ensemble of deep learning models Hit paper breakdown → | 2021 | 199 |
| 2 | 2018 | 40 | |
| 3 | 2021 | 37 | |
| 4 | 2019 | 27 | |
| 5 | 2020 | 24 | |
| 6 | 2019 | 19 | |
| 7 | 2023 | 11 | |
| 8 | 2013 | 4 | |
| 9 | 2010 | 0 | |
| 10 | 2013 | 0 | |
| 11 | 2008 | 0 |
About Gi-Tae Han
Gi-Tae Han is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition, Environmental Engineering, Automotive Engineering and Cardiology and Cardiovascular Medicine, having authored 11 papers that have together received 361 indexed citations. Recurring topics across this work include Non-Invasive Vital Sign Monitoring (4 papers), Advanced Chemical Sensor Technologies (3 papers), COVID-19 diagnosis using AI (2 papers), Remote Sensing and LiDAR Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Autonomous Vehicle Technology and Safety (2 papers), ECG Monitoring and Analysis (2 papers) and Lung Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (204 citations), Health Informatics (10 citations), Health Information Management (23 citations), Artificial Intelligence (150 citations) and Computer Vision and Pattern Recognition (60 citations). Gi-Tae Han has collaborated with scholars based in South Korea and India. Frequent co-authors include Zong Woo Geem, Ram Sarkar, Rohit Kundu, Seong-Hoon Kim, Arpan Basu and Jin‐Woo Hong. Their work appears in journals such as Sensors, PLoS ONE, Swarm and Evolutionary Computation, Diagnostics and KIPS Transactions on Software and Data Engineering.
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.