Pranjal Sahu, Ph.D. is a senior R&D engineer on Kitware’s Medical Computing Team located in Carrboro, North Carolina. He helps develop advanced image analysis and machine learning algorithms that will be deployed as part of software applications in the field of medicine.
Before joining Kitware, Pranjal worked at the Samsung Research Institute in India on their Android software development team. While earning his Ph.D., Pranjal also participated in a couple of internships, the first at Brookhaven National Laboratory, where he worked on the autonomous infrastructure for X-ray scattering experiments at NSLS II. He also interned at Siemens Healthineers, where he was involved in the Siemens lung CAD project.
Pranjal holds a Ph.D. in computer science from Stony Brook University. In his thesis, he tackled the problem of data scarcity in medical imaging by exploring various ways of utilizing synthetic/augmented data while training deep neural network models. Some of the problems he worked on included 3D lung nodule classification, lung volume segmentation, and DBT reconstruction. He received his Bachelor of Technology degree in computer science and engineering from the Indian Institute of Technology Kharagpur.
For more information on Pranjal’s work and research, please refer to his publication section or to his personal website.
Best Demo Award presented by Live Demonstration Workshop, SPIE Medical Imaging, 2018
Invited Talks & Media
Invited talk, Deep Learning Applications in Medical Imaging, Bell Labs Murray Hill, 2019
- P. Sahu, Y. Zhao, P. Bhatia, L. Bogoni, A. Jerebko, and H. Qin, "Structure Correction for Robust Volume Segmentation in Presence of Tumors," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 4, pp. 1151-1162, Apr. 2021. [URL]
- H. Huang, X. Duan, P. Sahu, and W. Zhao, "Effect of scatter correction on image noise in contrast-enhanced digital breast tomosynthesis," in 15th International Workshop on Breast Imaging (IWBI2020), 2020. [URL]
- X. Duan, P. Sahu, H. Huang, and W. Zhao, "Scatter correction with deep learning approach for contrast enhanced digital breast tomosynthesis (CEDBT) in both cranio-caudal (CC) view and mediolateral oblique (MLO) view," in 15th International Workshop on Breast Imaging (IWBI2020), 2020. [URL]
- M. Dasari, A. Bhattacharya, S. Vargas, P. Sahu, A. Balasubramanian, and S. Das, "Streaming 360-Degree Videos Using Super-Resolution," in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020. [URL]
- P. Sahu, D. Yu, M. Dasari, F. Hou, and H. Qin, "A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation," IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 960-968, May 2019. [URL]
- P. Sahu, H. Huang, W. Zhao, and H. Qin, "Using Virtual Digital Breast Tomosynthesis for De-Noising of Low-Dose Projection Images," in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019. [URL]
- P. Sahu, D. Yu, and H. Qin, "Apply lightweight deep learning on internet of things for low-cost and easy-to-access skin cancer detection," in Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 2018. [URL]
- P. Sahu, D. Yu, K. Yager, M. Dasari, and H. Qin, "In-Operando Tracking and Prediction of Transition in Material System using LSTM," in Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science, 2018. [URL]
- N. Song, D. Craciun, C. Christoffer, X. Han, D. Kihara, G. Levieux, M. Montes, H. Qin, P. Sahu, G. Terashi, and H. Liu, "Protein Shape Retrieval," Eurographics Workshop on 3D Object Retrieval, pp. 8 pages, 2017. [URL]