Session Chairs: Khan M. Iftekharuddin, Old Dominion Univ. (United States), Andrzej Krol, SUNY Upstate Medical University (United States), and Aaron D. Ward, The Univ. of Western Ontario (Canada)


8:00 AM:
Welcome and Introduction

8:05 AM:
Robert F. Wagner Award Finalists Announcements for Conferences 12465, 12468, and 12471

Computer-Aided Diagnosis Best Paper Award Announcement
Award Sponsored by:

8:10 AM
From code to clinic: challenges in translating ML models into real-world products

Dale Webster, Google Health (United States)

Inspired by the potential of artificial intelligence (AI) to improve access to expert-level medical image interpretation, several organizations began developing deep learning-based AI systems around 2015. Today, these AI-based tools are finally being deployed at scale in certain parts of the world, often bringing screening to populations lacking easy access to timely diagnosis. The path to translating AI research into a useful clinical tool has gone through several unforeseen challenges along the way. In this talk, we share some lessons contrasting a priori expectations (“myths”) with synthesized learnings of what truly transpired (“reality”), to help others who wish to develop and deploy medical AI tools

Dale Webster is a research director at Google Health working to bring AI based analysis of Medical Imaging to the world using Deep Learning. Prior to that he was a Software Engineer at Pacific Biosciences working on DNA sequencing and genomics. His PhD work in Bioinformatics at the University of California San Francisco focused on viral evolution.

This keynote is part of the Computer-Aided Diagnosis conference.



8:50 AM
Clinical applications of fast and quantitative MR fingerprinting

Dan Ma Case Western Reserve Univ., School of Medicine (United States)

MR Fingerprinting is a quantitative MR imaging technique that provides multiple tissue property maps simultaneously after a single MR scan. This scan is clinically feasible and has been robustly applied to imaging different parts of the body and various diseases. In multisite studies from multiple vendors, the MRF technique demonstrated high reproducibility for T1 and T2 relaxation time mapping despite scanner variations and other confounding factors. MRF thus offers a unique opportunity to ensure quality control of the MRI source data for the following analysis and clinical decision support by providing reliable and reproducible measurements of tissue properties. Combining the precision and sensitivity of MRF with advanced image analysis techniques may lead to a unified quantitative imaging tool for precision cancer imaging and treatment planning in multisite studies.

Dan Ma, PhD, Assistant Professor, Department of Biomedical Engineering, School of Medicine, Case Western Reserve University, and Member, Cancer Imaging Program, Case Comprehensive Cancer Center. Research Interests: Magnetic Resonance Imaging (MRI), Magnetic Resonance Fingerprinting (MRF), Quantitative MR, and Neuroimaging. Research Projects: Multi-parametric Quantitative MR Imaging, MR Fingerprinting, in collaboration with Siemens, MR Fingerprinting Quantum Optimization, in collaboration with Microsoft, MR Fingerprinting in Epilepsy, in collaboration with Epilepsy Center at Cleveland Clinic, and Brain Tumors, Multiple Sclerosis, Alzheimer's disease. Awards and Honors: Outstanding Teacher Award, 2018, International Society for Magnetic Resonance in Medicine; Junior Fellow 2017, International Society for Magnetic Resonance in Medicine; I.I.Rabi Young Investigator Award 2016, International Society for Magnetic Resonance in Medicine; and Hiroyuki Fujita PhD Award 2012 Case Western Reserve University.

This keynote is part of the Biomedical Applications in Molecular, Structural, and Functional Imaging conference.



9:30 AM
Translating computational innovations into reality: focus on the users!

Elizabeth A. Krupinski, Emory Univ. School of Medicine (United States)

Computer-aided/AI-driven tools are increasing being developed for use with digital pathology images. Whether a given scheme makes it into clinical use depends on a multitude of factors, perhaps most importantly whether it has an impact on a clinician’s decision-making process thus on patient care and outcomes. To have a positive impact, clinical decision support tools must be well-integrated into routine clinical workflows and thus require assessment from a human factors perspective that includes attention to ways these tools impact users’ perceptual and cognitive information processing mechanisms. Methods from implementation sciences (the scientific study of methods to promote systematic uptake of research findings and other evidence-based practices into routine practice to improve the quality and effectiveness of health services) can also be used to prepare users for and then assess the impact of introducing these tools into the clinical workflow. This talk will provide an overview of these perspectives, drawing on the history of medical image perception research in radiology and the growing application of these principles and methods in pathology.

Elizabeth Krupinski is Professor and Vice-Chair of Research at Emory University in the Departments of Radiology, Psychology and Bioinformatics. She received her BA from Cornell, MA from Montclair State and PhD from Temple, all in Experimental Psychology. Her interests are in medical image perception, observer performance, decision making, human factors, and computer-aided/AI-driven tools and how they impact efficacy and efficiency of medical decision making. She is Past President of the American Telemedicine Society, Past Chair of Society for Imaging Informatics in Medicine, Past Chair of SPIE Medical Imaging, President of the Society for Education and the Advancement of Connected Health and President of the Medical Image Perception Society. She is an SPIE Board Member and Associate Editor for the SPIE Journal of Medical Imaging (JMI).

This keynote is part of the Digital and Computational Pathology conference.