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SUMMARY:Computer Science Student Defense:: Vishwa Parekh\, Johns Hopkins University – “Integrated Graph Theoretic\, Radiomics and Deep Learning Framework for Personalized Radiological Diagnosis\, Prognosis and Treatment Response Assessment of Body Tumors”
DESCRIPTION:LocationMalone 228AbstractA new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer science and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced computational algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework for personalized clinical diagnosis\, prognosis and treatment planning using an integration of graph theory\, radiomics and deep learning (I-GRAD).BioVishwa Parekh is a PhD candidate in Computer Science at JHU. He is primarily advised by Dr. Michael Jacobs and co-advised by Dr. Russell Taylor and Dr. Jerry Prince. Vishwa received a B.E. in Computer Science from BITS\, Pilani in 2011 and a M.S.E in Computer Science from JHU in 2013.Vishwa’s research interest lies in developing techniques that enable us to “see” patterns in high dimensional imaging data that are not visually perceivable to naked eye. During his Ph.D.\, Vishwa published 5 journal papers\, 2 conference papers\, 8 abstracts and filed 4 patents. His research in manifold and deep learning was covered in AuntMinnie.com for “on the road to RSNA” for years 2015 and 2017. In addition\, his work on manifold learning in prostate imaging was selected for power pitch presentation (top 2%) at The International Society for Magnetic Resonance in Medicine in 2017.
URL:https://www.cs.jhu.edu/event/computer-science-student-defense-vishwa-parekh-johns-hopkins-university-integrated-graph-theoretic-radiomics-and-deep-learning-framework-for-personalized-radiological-diagnosis/
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