Ujjwal Baid
Impact in
- Health Informatics top 2%
- Neurology top 5%
- Brain Tumor Detection and Classification
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 23
- COVID-19 diagnosis using AI 7
- Neurology 15
- Brain Tumor Detection and Classification 15
- Co-authors
- Sanjay Nilkanth Talbar (20 shared papers)Abhishek Mahajan (4 shared papers)Aliasgar V. Moiyadi (3 shared papers)Swapnil Ulhas Rane (3 shared papers)Sudeep Gupta (3 shared papers)Meenakshi Thakur (3 shared papers)Bhakti Baheti (8 shared papers)Spyridon Bakas (7 shared papers)
- Journals
- Neuro-Oncology (4 papers)Radiology Artificial Intelligence (2 papers)The Lancet Oncology (2 papers)Nature Communications (2 papers)Nature Machine Intelligence (1 paper)
- Partner nations
- United StatesIndiaGermany
In The Last Decade
Ujjwal Baid
50 papers receiving 1.2k citations
Ujjwal Baid's Hit Papers
Peers
Comparison fields: 5 of 95
- Health Informatics 92
- Neurology 252
- Radiology, Nuclear Medicine and Imaging 483
- Genetics 165
- Computer Vision and Pattern Recognition 275
Countries citing papers authored by Ujjwal Baid
This map shows the geographic impact of Ujjwal Baid'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 Ujjwal Baid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ujjwal Baid more than expected).
Fields of papers citing papers by Ujjwal Baid
This network shows the impact of papers produced by Ujjwal Baid. 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 Ujjwal Baid. The network helps show where Ujjwal Baid may publish in the future.
Co-authors
The 25 scholars most cited alongside Ujjwal Baid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Federated learning enables big data for rare cancer boundary detection Hit paper breakdown → | 2022 | 251 |
| 2 | 2020 | 98 | |
| 3 | 2022 | 95 | |
| 4 | 2023 | 93 | |
| 5 | 2020 | 86 | |
| 6 | 2021 | 71 | |
| 7 | 2022 | 51 | |
| 8 | 2019 | 46 | |
| 9 | 2021 | 38 | |
| 10 | 2022 | 37 | |
| 11 | 2023 | 28 | |
| 12 | 2021 | 27 | |
| 13 | 2022 | 26 | |
| 14 | 2023 | 21 | |
| 15 | 2019 | 18 | |
| 16 | 2024 | 18 | |
| 17 | 2023 | 18 | |
| 18 | 2017 | 17 | |
| 19 | 2020 | 15 | |
| 20 | 2024 | 15 |
About Ujjwal Baid
Ujjwal Baid is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology, Genetics, Computer Vision and Pattern Recognition and Health Informatics, having authored 53 papers that have together received 1.2k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (23 papers), Brain Tumor Detection and Classification (15 papers), Glioma Diagnosis and Treatment (14 papers), Advanced Neural Network Applications (8 papers), AI in cancer detection (8 papers), COVID-19 diagnosis using AI (7 papers), Medical Image Segmentation Techniques (7 papers) and Artificial Intelligence in Healthcare and Education (5 papers). The work is most often cited by research in Health Informatics (92 citations), Neurology (252 citations), Radiology, Nuclear Medicine and Imaging (483 citations), Genetics (165 citations) and Computer Vision and Pattern Recognition (275 citations). Ujjwal Baid has collaborated with scholars based in United States, India and Germany. Frequent co-authors include Sanjay Nilkanth Talbar, Abhishek Mahajan, Aliasgar V. Moiyadi, Swapnil Ulhas Rane, Sudeep Gupta, Meenakshi Thakur, Bhakti Baheti, Spyridon Bakas, Evan Calabrese and Javier E. Villanueva-Meyer. Their work appears in journals such as Neuro-Oncology, Radiology Artificial Intelligence, The Lancet Oncology, Nature Communications and Nature Machine 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.