Debesh Jha
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 2%
- Oncology top 5%
- Neurology top 5%
- Co-authors
- Pål HalvorsenMichael A. RieglerNikhil Kumar TomarSharib AliHåvard D. JohansenDag JohansenUlaş BağcıJens Rittscher
- Topics
- Radiomics and Machine Learning in Medical Imaging (19 papers)AI in cancer detection (16 papers)Colorectal Cancer Screening and Detection (14 papers)
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Partner nations
- United StatesNorwayUnited Kingdom
In The Last Decade
Debesh Jha
52 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Artificial Intelligence 637
- Radiology, Nuclear Medicine and Imaging 623
- Computer Vision and Pattern Recognition 586
- Oncology 530
- Neurology 221
Countries citing papers authored by Debesh Jha
This map shows the geographic impact of Debesh Jha'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 Debesh Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debesh Jha more than expected).
Fields of papers citing papers by Debesh Jha
This network shows the impact of papers produced by Debesh Jha. 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 Debesh Jha. The network helps show where Debesh Jha may publish in the future.
Co-authorship network of co-authors of Debesh Jha
This figure shows the co-authorship network connecting the top 25 collaborators of Debesh Jha. A scholar is included among the top collaborators of Debesh Jha 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 Debesh Jha. Debesh Jha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 12 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 3 | |
| 13 | 64 | |
| 14 | 90 | |
| 15 | 51 | |
| 16 | 12 | |
| 17 | Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learningbreakdown → | 237 |
| 18 | HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopybreakdown → | 261 |
| 19 | 2 | |
| 20 | The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract Using Global Features and Deep Learning. | 1 |
About Debesh Jha
Debesh Jha is a scholar working on Health Informatics, Neurology and Radiology, Nuclear Medicine and Imaging, having authored 57 papers that have together received 1.6k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (19 papers), AI in cancer detection (16 papers) and Colorectal Cancer Screening and Detection (14 papers). The work is most often cited by research in Health Informatics (45 citations), Computer Vision and Pattern Recognition (586 citations) and Radiology, Nuclear Medicine and Imaging (623 citations). Debesh Jha has collaborated with scholars based in United States, Norway and United Kingdom. Frequent co-authors include Pål Halvorsen, Michael A. Riegler, Nikhil Kumar Tomar, Sharib Ali, Håvard D. Johansen, Dag Johansen, Ulaş Bağcı, Jens Rittscher, Goo‐Rak Kwon and Thomas de Lange. Their work appears in journals such as Gastroenterology, IEEE Access and IEEE Transactions on Medical Imaging.
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.