Biomedical Sensing with Digital Fingerprinting Tool Leveraging Microwave Imaging and Machine Learning

Title: Biomedical Sensing with Digital Fingerprinting Tool Leveraging Microwave Imaging and Machine Learning

Abstract: Microwaves are non-ionizing, time-varying, electromagnetic waves within a frequency spectrum of 300 MHz to 300 GHz. They are widely used in various industries, including sensing, telecommunications, weather forecasting, food industry, defence, manufacturing, and precision agriculture. Microwave applications in biomedical fields are relatively still at a rudimentary stage, with a growing interest in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Recent advances in machine learning have enabled microwave systems to augment towards healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. Digitization of tissues allows examination of tissue morphologies in new ways enabling patient stratification for effective treatments. Current slide-scanning techniques capture the visible details of the tissue as whole-slide images and digitally record them in the form of spatial and color relationships. Specialized experimental techniques like dielectric spectroscopy can also be used to investigate a tissue’s response to an applied electric field. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding such differences through machine learning based classification models can be a key for identifying the disease state(s). This technology pipeline, thus, shows great potentials for developing the next generation non-invasive diagnostic tools for therapeutic intervention.

Biography: Dr. Sayan Roy is currently an Assistant Professor of Electrical and Computer Engineering in the School of Science and Engineering at the University of Missouri-Kansas City (UMKC), USA. Dr. Roy received a B.Tech. degree in Electronics and Communications Engineering from West Bengal University of Technology, Kolkata, India, in 2010, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from North Dakota State University, USA, in 2012, and 2017, respectively. Before joining UMKC, Dr. Roy served in various academic and research positions at the University of North Dakota, Johns Hopkins University, South Dakota School of Mines and Technology, and Mayo Clinic. Dr. Roy’s technical interests including research and teaching are focused on applied electromagnetics. Dr. Roy has been awarded multiple federal and industrial grants of totaling over $1.5 million, published more than 90 journal papers, conference proceedings, and book chapters, delivered more than 30 keynotes, invited talks/seminars, and advised 15 graduate level students. As a Senior Member of IEEE, Dr. Roy is currently serving as the Regional Coordinator of Region 4 of IEEE MTT society, technical program committee member of various IEEE conferences, and reviewer of multiple IEEE journals and magazines. Dr. Roy is also a member of IEEE HKN, a Senior Member of URSI, and a Full Member of Commission K, USNC-URSI.