Education

Doctor of Philosophy
Computer Science, Johns Hopkins University, Baltimore, MD 2013-2018
Master of Science and Engineering
Computer Science, Johns Hopkins University, Baltimore, MD 2011-2013
Bachelor of Engineering (Hons.)
Computer Science, Birla Institute of Technology and Science, Pilani; RAJ, India 2007-2011

Areas of Interest

Research Interests

My research focuses on building a collaborative multi-omics precision medicine platform for clinical decision support, with the overarching goal of bringing AI from bench to bedside. This includes methods to improve AI model development (e.g., weakly-supervised learning to reduce human annotation burden on radiology images), federated learning frameworks to train cross-domain “foundation AI models” from distributed research groups focusing on their own “narrow” tasks while preserving patient privacy, lifelong learning methods to continually learn and update AI model knowledge based on new imaging protocols and diseases, e.g., COVID-19 (just like how humans continually learn), and multiomics precision medicine for improved clinical decision support. Over the years, I have worked within many collaborative teams on numerous healthcare applications including extended reality application development for telemedicine, diagnosis, prognosis, and treatment responses assessment of body cancers, diagnosis of pancreatitis, characterization of muscular dystrophy, and sleep disorder breathing. In addition, my work has been recognized with numerous honors, including spotlight presentations at international conferences, including the ISMRM and Medical Imaging with Deep Learning (MIDL), as well as numerous patents encompassing the fields of AI, Radiology, and Oncology.

Publications

PUBLICATIONS (In Reverse Chronological Order):

Peer-reviewed journal publications
1. Kamel, P., Kanhere, A., Kulkarni, P., Khalid, M., Steger, R., Bodanapally, U., Gandhi, D., Parekh, V., & Yi, P. H. Optimizing Acute Stroke Segmentation on MRI using Deep Learning: Self-configuring Neural Networks Provide High Performance using only DWI Sequences. Journal of Digital Imaging. 2023 (In Press)
2. Wen Li, Savannah C Partridge, David C Newitt, Jon Steingrimsson, Helga S Marques, Patrick Bolan, Michael Hirano, Benjamin Aaron Bearce, Jayashree Kalpathy-Cramer, Michael A Boss, Xinzhi Teng, Jiang Zhang, Jing Cai, Despina Kontos, Eric A Cohen, Walter C Mankowski, Michael Liu, Richard Ha, Oscar J Pellicer-Valero, Klaus Maier-Hein, Simona Rabinovici-Cohen, Tal Tlusty, Michal Ozery-Flato, Vishwa S Parekh, Michael A Jacobs, Ran Yan, Kyunghyun Sung, Anum S Kazerouni, Julie C DiCarlo, Thomas E Yankeelov, Thomas L Chenevert, Nola M Hylton. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: the BMMR2 Challenge. Radiology Imaging Cancer. 2023 (In Press)
3. Doo FX, Kulkarni P, Siegel E, Toland M, Paul HY, Carlos RC, Parekh VS. Economic and environmental costs of cloud for medical imaging and radiology artificial intelligence. Journal of the American College of Radiology. 2023 Dec 9.
4. Doo FX, Parekh VS, Kanhere A, Savani D, Tejani AS, Sapkota A, Paul HY. Evaluation of Climate-Aware Metrics Tools for Radiology Informatics and Artificial Intelligence: Towards a Potential Radiology Eco-Label. Journal of the American College of Radiology. 2023 Dec 1.
5. Bachina P, Garin SP, Kulkarni P, Kanhere A, Sulam J, Parekh VS, Paul HY. Coarse Race and Ethnicity Labels Mask Granular Underdiagnosis Disparities in Deep Learning Models for Chest Radiograph Diagnosis. Radiology. 2023 Nov;309(2):e231693.
6. Cao Y, Parekh VS, Lee E, Chen X, Redmond KJ, Pillai JJ, Peng L, Jacobs MA, Kleinberg LR. A Multidimensional Connectomics-and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases. Cancers. 2023 Aug 15;15(16):4113.
7. Garin SP, Parekh VS, Sulam J, Paul HY. Medical imaging data science competitions should report dataset demographics and evaluate for bias. Nature medicine. 2023 May;29(5):1038-9.
8. Santomartino SM, Hafezi-Nejad N, Parekh VS, Yi PH. Performance and Usability of Code-Free Deep Learning for Chest Radiograph Classification, Object Detection, and Segmentation. Radiology: Artificial Intelligence. 2023 Feb 15;5(2):e220062.
9. Beheshtian E, Putman K, Santomartino SM, Parekh VS, Yi PH. Generalizability and bias in a deep learning pediatric bone age prediction model using hand radiographs. Radiology. 2022 Sep 27:220505.
10. Parekh VS, Pillai JJ, Macura KJ, LaViolette, PS, Jacobs MA. Tumor Connectomics: Mapping the intra-tumoral complex interaction network using machine learning. Cancers 14(6), 2022

Peer-reviewed conference publications
1. Zheng G, Jacobs MA, Braverman V, Parekh VS. Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. International Workshop on Federated Learning for Distributed Data Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023
2. Zheng G, Zhou S, Braverman V, Jacobs MA, Parekh VS. Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. International conference on Medical Imaging with Deep Learning (MIDL) 2023 Apr 4.
3. Kulkarni P, Kanhere A, Yi PH, Parekh VS. From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning. MedNeurIPS workshop at NeurIPS, 2022.