Joseph Bae
Impact in
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- Head and Neck Cancer Studies
Papers in
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- Radiomics and Machine Learning in Medical Imaging 9
- COVID-19 diagnosis using AI 6
- MRI in cancer diagnosis 2
- Co-authors
- Prateek Prasanna (12 shared papers)Huidong Liu (3 shared papers)Dimitris Samaras (3 shared papers)Lei Zhou (3 shared papers)Junjun He (2 shared papers)Abhinav Vepa (1 shared paper)Faheem Ahmed (1 shared paper)Kamlesh Khunti (1 shared paper)
- Journals
- International Journal of Radiation Oncology*Biology*Physics (2 papers)Journal of Clinical Medicine (1 paper)Advances in Radiation Oncology (1 paper)Diabetes & Metabolic Syndrome Clinical Research & Reviews (1 paper)Polymers for Advanced Technologies (1 paper)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Joseph Bae
15 papers receiving 233 citations
Peers
Comparison fields: 5 of 72
- Health Informatics 8
- Otorhinolaryngology 17
- Radiology, Nuclear Medicine and Imaging 86
- Neurology 25
- Computer Vision and Pattern Recognition 51
Countries citing papers authored by Joseph Bae
This map shows the geographic impact of Joseph Bae'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 Joseph Bae with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph Bae more than expected).
Fields of papers citing papers by Joseph Bae
This network shows the impact of papers produced by Joseph Bae. 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 Joseph Bae. The network helps show where Joseph Bae may publish in the future.
Co-authors
The 25 scholars most cited alongside Joseph Bae, 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 | 80 | |
| 2 | 2020 | 45 | |
| 3 | 2021 | 24 | |
| 4 | 2008 | 21 | |
| 5 | 2023 | 17 | |
| 6 | 2020 | 15 | |
| 7 | 2022 | 15 | |
| 8 | 2009 | 10 | |
| 9 | 2021 | 5 | |
| 10 | Predicting COVID-19 Lung Infiltrate Progression on Chest Radiographs Using Spatio-temporal LSTM based Encoder-Decoder Network | 2021 | 4 |
| 11 | 2024 | 3 | |
| 12 | 2024 | 1 | |
| 13 | 2021 | 1 | |
| 14 | 2025 | 1 | |
| 15 | 2022 | 1 | |
| 16 | 2023 | 0 | |
| 17 | 2024 | 0 | |
| 18 | 2024 | 0 |
About Joseph Bae
Joseph Bae is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Otorhinolaryngology, having authored 18 papers that have together received 243 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), COVID-19 diagnosis using AI (6 papers), Advanced Neural Network Applications (3 papers), Head and Neck Cancer Studies (2 papers), Medical Image Segmentation Techniques (2 papers), Brain Tumor Detection and Classification (2 papers), MRI in cancer diagnosis (2 papers) and Glioma Diagnosis and Treatment (2 papers). The work is most often cited by research in Health Informatics (8 citations), Otorhinolaryngology (17 citations), Radiology, Nuclear Medicine and Imaging (86 citations), Neurology (25 citations) and Computer Vision and Pattern Recognition (51 citations). Joseph Bae has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Prateek Prasanna, Huidong Liu, Dimitris Samaras, Lei Zhou, Junjun He, Abhinav Vepa, Faheem Ahmed, Kamlesh Khunti, Manish Pareek and Xuan Xu. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, Journal of Clinical Medicine, Advances in Radiation Oncology, Diabetes & Metabolic Syndrome Clinical Research & Reviews and Polymers for Advanced Technologies.
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.