Mini-Grand Rounds: Aftershocks: The Coronavirus Pandemic and The New World Disorder
Colin H. Kahl
Senior Fellow at the Freeman Spogli Institute for International Studies
Steven C. Házy Senior Fellow at the Center for International Security and Cooperation
Professor, by courtesy, of Political Science
Co-director of the Center for International Security and Cooperation
7:00am – 7:30am, Zoom
The Stanford Radiology Mini-Grand Round live session events are by invitation only. Invites with link to Zoom video will be sent via email to Department faculty and staff only. Recordings will be made available to the public shortly after the event.
Mini-Grand Rounds: The short-run challenges and long-run opportunities of working from home
Nicholas Bloom, PhD
Professor (by courtesy), Economics
Senior Fellow, Stanford Institute for Economic Policy Research
7:00am – 7:30am, Zoom
The Stanford Radiology Mini-Grand Round live session events are by invitation only. Invites with link to Zoom video will be sent via email to Department faculty and staff only. Recordings will be made available to the public shortly after the event.
Radiomics and Radio-Genomics: Opportunities for Precision Medicine
Zoom: https://stanford.zoom.us/j/99904033216?pwd=U2tTdUp0YWtneTNUb1E4V2x0OTFMQT09
Pallavi Tiwari, PhD
Assistant Professor of Biomedical Engineering
Associate Member, Case Comprehensive Cancer Center
Director of Brain Image Computing Laboratory
School of Medicine | Case Western Reserve University
Abstract:
In this talk, Dr. Tiwari will focus on her lab’s recent efforts in developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging. She will focus on clinical applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She will also discuss current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment. Dr. Tiwari will conclude with a discussion on her lab’s findings in AI + experts, in the context of a clinically challenging problem of post-treatment response assessment on routine MRI scans.
Mini-Grand Rounds: Stanford University Medical Center and COVID-19: A Chest Radiologist’s Perspective
Ann Leung, MD
Associate Chair, Clinical Affairs
Professor, Radiology
7:00am – 7:30am, Zoom
The Stanford Radiology Mini-Grand Round live session events are by invitation only. Invites with link to Zoom video will be sent via email to Department faculty and staff only. Recordings will be made available to the public shortly after the event.
Mini-Grand Rounds: The Outlook for Radiology in the Next Phases of the Pandemic and Beyond
David Larson, MD, MBA
Vice Chair, Education and Clinical Operations
Associate Professor, Radiology
7:00am – 7:30am, Zoom
The Stanford Radiology Mini-Grand Round live session events are by invitation only. Invites with link to Zoom video will be sent via email to Department faculty and staff only. Recordings will be made available to the public shortly after the event.
CEDSS: “Multicancer detection of early-stage cancers with simultaneous tissue localization using a plasma cfDNA-based targeted methylation assay”
Eric Fung, M.D., Ph.D.
Senior Medical Director
GRAIL, Inc.
Please see zoom details below:
Meeting URL: https://stanford.zoom.us/j/230531527
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
Meeting ID: 230 531 527
ABOUT
Dr. Eric Fung is Vice President, Clinical Development at GRAIL, where he leads several clinical development programs in support of the development of a blood-based multi-cancer detection test. Dr. Fung has previously held clinical development and R&D leadership roles at Affymetrix, Vermillion, Ciphergen, and Roche Molecular Diagnostics. Dr. Fung has led clinical trials leading to FDA clearance of multiple IVD products. Dr. Fung received his MD, PhD from the Johns Hopkins University School of Medicine.
Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by the Canary Center & the Department of Radiology
Stanford University – School of Medicine
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
CEDSS: “The Origins and Detection of Lethal Prostate Cancer”
Paul Boutros, Ph.D., M.B.A.
Director, Cancer Data Sciences
UCLA
Please see zoom details below:
Meeting URL: https://stanford.zoom.us/s/93515779500
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 935 1577 9500
Meeting Passcode: 767148
ABOUT
Boutros earned his B.Sc. degree from the University of Waterloo in Chemistry in 2004, and his Ph.D. degree from the University of Toronto, Canada, in Medical Biophysics in 2008. At Toronto, he also earned an executive M.B.A. from the Rothman School of Management. In 2008, Boutros started his independent research career at the Ontario Institute for Cancer Research first as a fellow (2008–2010) and then as principal investigator (2010–2018). He moved to California to join the UCLA faculty in 2018.
Hosted by: Utkan Demirci, Ph.D.
Sponsored by the Canary Center & the Department of Radiology
Stanford University – School of Medicine
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.
CEDSS: Systematic identification of fluid-based biomarkers for ovarian and prostate cancer
Thomas Kislinger, Ph.D.
Professor & Chair
Department of Medical Biophysics
University of Toronto
Senior Scientist
Princess Margaret Cancer Centre
Zoom Webinar Details
Meeting URL: https://stanford.zoom.us/s/94878578384
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 948 7857 8384
Passcode: 692692
Register Here
ABOUT
Thomas Kislinger received his MSc in Analytical Chemistry from the University of Munich, Germany (1998). He completed his PhD in 2001, investigating the role of Advanced Glycation Endproducts in diabetic vascular complications at the University of Erlangen, Germany and Columbia University, New York. Between 2002 and 2006 he completed a post-doctoral fellowship at the University of Toronto. In 2006 he joined the Princess Margaret Cancer Centre as an independent investigator. Dr. Kislinger holds positions as Senior Scientist at the Princess Margaret Cancer Centre and as Professor and Chair at the University of Toronto in the Department of Medical Biophysics. The Kislinger lab applies proteomics technologies to translational and basic cancer biology. This includes the development of novel proteomics methodologies, identification of liquid biopsy signatures and the molecular identification of novel cell surface markers.
Hosted by: Utkan Demirci, Ph.D.
Sponsored by: The Canary Center & the Department of Radiology
Stanford University – School of Medicine