Dr. Fleming Y. M. Lure received his Ph.D. degree in electrical engineering from Pennsylvania State University, State College, PA, USA, in 1990. He is a Chief Product Officer with MS Technologies Corporation, Rockville, MD, USA. His research interests include computer-aided detection (CAD), machine learning, and deep learning for disease detection, diagnosis, and prognosis on radiological images and portable devices. He has led a team to receive the first FDA pre-market approved (PMA) early-stage lung cancer detection system on radiograph. He has received more than ten SBIR/STTR Phase I/II awards from NIH and DoD to develop and commercialize computer-aided diagnosis (CAD) systems on lung cancer, tuberculosis, and Alzheimer’s disease on radiological images and fall detection from low-cost portable radar. He is also leading a team with interdisciplinary backgrounds to develop a smart imagery framing and truthing (SIFT) system to automatically annotate large amounts of COVID and tuberculosis radiographs to support various governments and consortiums to provide high-quality annotated information for the training of artificial intelligence (AI) and clinical researchers worldwide. He is a member of the Radiological Society of North America (RSNA).
Dr. Fleming Y. M. Lure received his Ph.D. degree in electrical engineering from Pennsylvania State University, State College, PA, USA, in 1990. He is a Chief Product Officer with MS Technologies Corporation, Rockville, MD, USA. His research interests include computer-aided detection (CAD), machine learning, and deep learning for disease detection, diagnosis, and prognosis on radiological images and portable devices. He has led a team to receive the first FDA pre-market approved (PMA) early-stage lung cancer detection system on radiograph. He has received more than ten SBIR/STTR Phase I/II awards from NIH and DoD to develop and commercialize computer-aided diagnosis (CAD) systems on lung cancer, tuberculosis, and Alzheimer’s disease on radiological images and fall detection from low-cost portable radar. He is also leading a team with interdisciplinary backgrounds to develop a smart imagery framing and truthing (SIFT) system to automatically annotate large amounts of COVID and tuberculosis radiographs to support various governments and consortiums to provide high-quality annotated information for the training of artificial intelligence (AI) and clinical researchers worldwide. He is a member of the Radiological Society of North America (RSNA).