Judy Gichoya, MD
Assistant Professor
Emory University School of Medicine
Measuring Learning Gains in Man-Machine Assemblage When Augmenting Radiology Work with Artificial Intelligence
Abstract
The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains – will humans be augmented by technology or will technology be augmented by humans? We present our work on overcoming the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. We describe how learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. Our work is to demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs.
Specifically, we are evaluating the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a “good fit” for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience?
Zoom: https://stanford.zoom.us/j/93580829522?pwd=ZVAxTCtEdkEzMWxjSEQwdlp0eThlUT09
Ge Wang, PhD
Clark & Crossan Endowed Chair Professor
Director of the Biomedical Imaging Center
Rensselaer Polytechnic Institute
Troy, New York
Abstract:
AI-based tomography is an important application and a new frontier of machine learning. AI, especially deep learning, has been widely used in computer vision and image analysis, which deal with existing images, improve them, and produce features. Since 2016, deep learning techniques are actively researched for tomography in the context of medicine. Tomographic reconstruction produces images of multi-dimensional structures from externally measured “encoded” data in the form of various transforms (integrals, harmonics, and so on). In this presentation, we provide a general background, highlight representative results, and discuss key issues that need to be addressed in this emerging field.
About:
AI-based X-ray Imaging System (AXIS) lab is led by Dr. Ge Wang, affiliated with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute and the Center for Biotechnology and Interdisciplinary Studies in the Biomedical Imaging Center. AXIS lab focuses on innovation and translation of x-ray computed tomography, optical molecular tomography, multi-scale and multi-modality imaging, and AI/machine learning for image reconstruction and analysis, and has been continuously well funded by federal agencies and leading companies. AXIS group collaborates with Stanford, Harvard, Cornell, MSK, UTSW, Yale, GE, Hologic, and others, to develop theories, methods, software, systems, applications, and workflows.
Radiology Department-Wide Research Meeting
• Curt Langlotz, MD, PhD: Overview of the AIMI Center
• Brian Hargreaves, PhD: Research Details from Town Hall, Q&A, and COVID19 Updates
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Radiology Department-Wide Research Meeting
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
February 19 Speakers:
Bruce Daniel, MD – Center Overview: IMMERS
Jennifer McNab, PhD – Encoding and Decoding Diffusion MRI
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Radiology Department-Wide Research Meeting
• Dominik Fleischmann, MD: 3DQ Lab Overview
• Tom Soh, PhD: Research Updates
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Radiology Department-Wide Research Meeting
• Research Announcements
• Michelle James, PhD – Detecting and Tracking Immune Responses in the Brain and Beyond using PET
• Ryan Spitler, PhD – Precision Health and Integrated Diagnostics (PHIND) Center
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Targeted violence continues against Black Americans, Asian Americans, and all people of color. The department of radiology diversity committee is running a racial equity challenge to raise awareness of systemic racism, implicit bias and related issues. Participants will be provided a list of resources on these topics such as articles, podcasts, videos, etc., from which they can choose, with the “challenge” of engaging with one to three media sources prior to our session (some videos are as short as a few minutes). Participants will meet in small-group breakout sessions to discuss what they’ve learned and share ideas.
Please reach out to Marta Flory, flory@stanford.edu with questions. For details about the session, including recommended resources and the Zoom link, please reach out to Meke Faaoso at mfaaoso@stanford.edu.
Radiology Department-Wide Research Meeting
• Research Announcements
• Koen Nieman, M.D., PhD – Coronary Artery Disease by Computed Tomography
• Martin Willemink, M.D., PhD – Translating Emerging Cardiovascular CT Techniques into the Clinical Setting
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Daniel Ennis, PhD
Sponsored by: the the Department of Radiology
Join us for a panel on Behavioral XR on Thursday, June 3rd from 9:00 – 10:30 am PDT. The event will start with a one-hour panel discussion featuring Dr. Elizabeth McMahon, a psychologist with a private practice in California; Sarah Hill of Healium, a company developing XR apps for mental fitness based in Missouri; Christian Angern of Sympatient, a company developing VR for anxiety therapy based in Germany; and Marguerite Manteau-Rao of Penumbra, a medical device company based in California. This panel will be moderated by Dr. Walter Greenleaf of Stanford’s Virtual Human Interaction Lab (VHIL) and Dr. Christoph Leuze of the Stanford Medical Mixed Reality (SMMR) program. Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.
Register here to save your place now! After registering, you will receive a confirmation email containing information about joining the meeting.
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Sponsored by Stanford Medical Mixed Reality (SMMR)
Join us for the 11th biennial International Conference on Functional Imaging and Modeling of the Heart (FIMH). FIMH-2021 will celebrate 20 years of bringing together friends, colleagues, and collaborators to share and discuss the latest in cardiac and cardiovascular imaging, electrophysiology, computational modeling, and translational applications. The event will take place June 21-25, 2021 virtually, via Livestream, Zoom meeting workshops, and Spatial Chat networking.
Sponsored by: Functional Imaging and Modeling of the Heart Conference