Thursday MIPS Roundtable: Faculty Lab Showcase
1:30-2:00 PM | Dr. Raag Airan, M.D., Ph.D.
Airan Lab: Noninvasive Control of the Nervous System
Assistant Professor of Radiology and, by courtesy, of Materials Science and Engineering and of Psychiatry and Behavioral Sciences
Stanford University
2:00-2:30 PM | Dr. Daniel Spielman, Ph.D.
The Spielman Laboratory: In Vivo MR Spectroscopy and Multinuclear Imaging
Professor of Radiology and, by courtesy, of Electrical Engineering
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
5/7 Meeting URL: https://stanford.zoom.us/j/91812578814
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 918 1257 8814
Thursday MIPS Roundtable: Faculty Lab Showcase
1:30-2:00 PM | Dr. Gozde Durmus, Ph.D.
Levitating Cells for Applications in Biology and Medicine
Assistant Professor of Radiology
Stanford University
2:00-2:30 PM | Dr. Ramasamy Paulmurugan, Ph.D.
Cellular Pathway Imaging Laboratory (CPIL)
Associate Professor of Radiology
Stanford University
MIPS Roundtables will be every Thursday through May from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
5/14 Meeting URL: https://stanford.zoom.us/j/97661102825
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 976 6110 2825
Thursday MIPS Roundtable: Faculty Lab Showcase
1:30-2:00 PM | Dr. Sandip Biswal, M.D.
Associate Professor of Radiology
Stanford University
2:00-2:30 PM | Dr. Lei Xing, Ph.D.
Xing Lab: Where Sciences & Engineering Meet Medicine
Jacob Haimson Professor and Professor, by courtesy, of Electrical Engineering
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
5/21 Meeting URL: https://stanford.zoom.us/j/98018934679
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 980 1893 4679
Thursday MIPS Roundtable: Faculty Lab Showcase
1:30-2:00 PM | Dr. Jianghong Rao, Ph.D.
Cellular and Molecular Imaging Laboratory (CMIL)
Professor of Radiology and, by courtesy, of Chemistry
Stanford University
2:00-2:30 PM | Dr. Zhen Cheng, Ph.D.
Cancer Molecular Imaging Chemistry Laboratory (CMICL)
Associate Professor of Radiology
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
5/28 Meeting URL: https://stanford.zoom.us/j/92834097988
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 928 3409 7988
Thursday MIPS Roundtable: Meet our MIPS Instructors
1:30-2:00 PM | Dr. Katie Wilson, Ph.D.
“Optical and Acoustic Molecular Imaging to Identify Lymph Node Metastasis in Head and Neck Cancer.”
Instructor, Radiology
Stanford University
2:00-2:30 PM | Dr. Corinne Beinat, Ph.D.
“Molecular Imaging of Tumor Metabolism”
Instructor, Radiology
Stanford University
MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
6/11 Meeting URL: https://stanford.zoom.us/j/95475611159
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 954 7561 1159
Thursday MIPS Roundtable: Faculty Lab Showcase
MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
1:30-2:00 PM | Dr. Brian Rutt, Ph.D.
Cellular & Molecular MRI Laboratory (CMMRIL)
Professor of Radiology
Stanford University
2:00-2:30 PM | Dr. Kathy Ferrara, Ph.D.
Ferrara Laboratory: Image-guided Drug Delivery
Professor of Radiology
Stanford University
Please note Zoom information does change week to week.
6/25 Webinar URL: https://stanford.zoom.us/j/91635637393?pwd=c09vUXYyeU5VeHJBaUJVRHQrT3FJdz09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 916 3563 7393
Webinar Password: 271364
Thursday MIPS Roundtable: Meet our MIPS Instructors
MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested. Note we will take a break through late July and August.
1:30-2:00 PM | Dr. Ahmed El Kaffas, Ph.D.
Translational Ultrasound for Tissue Characterization and Stimulation
Instructor, Radiology
Stanford University
2:00-2:30 PM | Dr. Brett Fite, Ph.D.
Combining Focal and Immunotherapies
Instructor, Radiology
Stanford University
Please note Zoom information does change week to week.
7/9 Webinar URL: https://stanford.zoom.us/j/91909413178
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 919 0941 3178
Password: 572746
Thursday MIPS Roundtable: Meet our MIPS Instructors
MIPS Roundtables are Thursdays from 1:30-2:30pm showcasing various topics and are open to all interested. Note this will be our last summer Roundtable and we will take a break through late July and August.
1:30-2:00 PM | Dr. Josquin Foiret, Ph.D.
High throughput ultrasound imaging for improved diagnosis
Instructor, Radiology
Stanford University
2:00-2:30 PM | Dr. Jinghang Xie, Ph.D.
TESLA probes for imaging T cell-mediated cytotoxic response to immunotherapy
Instructor, Radiology
Stanford University
Please note Zoom information does change week to week.
7/16 Webinar URL: https://stanford.zoom.us/j/94952044130
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 949 5204 4130
Password: 963699
Location & Timing
August 5, 2020
8:30am-4:30pm
Livestream: details to come
This event is free and open to all!
Registration and Event details
Overview
Advancements of machine learning and artificial intelligence into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. Sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging, the 2020 AIMI Symposium is a virtual conference convening experts from Stanford and beyond to advance the field of AI in medicine and imaging. This conference will cover everything from a survey of the latest machine learning approaches, many use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing building and evaluating machine learning in healthcare applications.
Our goal is to make the best science accessible to a broad audience of academic, clinical, and industry attendees. Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
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