Scholar
Debesh Jha
Google Scholar ID: mMTyE68AAAAJ
University of South Dakota
Citations & Impact
Citations
9,827
H-index
33
i10-index
59
Publications
20
Co-authors
20
list available
Publications
Resume (English only)
Academic Achievements
- 1. Recognized among the world’s top 2% scientists by Stanford University and Elsevier.
- 2. Received A and S Professional Development Grant Program from the University of South Dakota for Spring 2025.
- 3. Elevated to IEEE Senior Member.
- 4. Three papers accepted at ICASSP 2025.
- 5. Three papers accepted at IEEE CVF WACV 2025.
- 6. DiffBoost accepted at IEEE TMI.
- 7. Poster of Distinction during Digestive Disease Week 2024.
- 8. Best Industry-Related Paper Award at ICPR 2024 for work on harmonized spatial and spectral learning for robust and generalized medical image segmentation.
- 9. Junior Distinguished Research and Development Award 2024 by the IEEE Chicago Section Award committee.
- 10. Five papers presented at MICCAI 2024.
- 11. Two papers accepted at the 2024 CVPR Workshop.
- 12. IEEE TMI Distinguished Reviewer Silver Level Award for 2023-2024.
- 13. Associate Editor for Frontiers in Radiation Oncology.
- 14. Associate Editor for Medical Physics Journal.
- 15. Kvasir-SEG dataset mentioned in the Artificial Intelligence Index Report 2022 from Stanford University.
- 16. Guest Editor for Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.
- 17. Guest Editor for Machine-Learning-Based Process and Analysis of Medical Images.
- 18. One paper accepted at MICCAI 2022.
Research Experience
- Prior to joining the University of South Dakota, he developed algorithms for colonoscopy and endoscopy, with ColonSegNet and Kvasir-SEG being included by NVIDIA Clara. He also investigates predictive modeling and organ-at-risk assessment to optimize radiation therapy planning and outcomes.
Education
- Information not provided
Background
- Primary research focuses on developing advanced AI algorithms to improve medical imaging, including upper and lower gastrointestinal imaging, lung and liver tumor analysis, and predictive modeling for radiation therapy outcomes. He addresses issues such as data scarcity, interobserver variability, biases, and limited generalizability.
Miscellany
- Personal interests include AI in sports analytics, using AI insights for performance optimization and strategy.
