Abstract: Gene editing with CRISPR technology is transforming biology. Understanding the underlying chemical mechanisms of RNA-guided DNA and RNA cleavage provides a foundation for both conceptual advances and technology development. I will discuss how bacterial CRISPR adaptive immune systems inspire creation of powerful genome editing tools, enabling advances in both fundamental biology and applications in medicine. I will also discuss the ethical challenges of some of these applications with a focus on what our decisions now might mean for future generations.
About: MIPS IMAGinING THE FUTURE seminar series is aimed at catalyzing interdisciplinary discussions in all area of medicine and disease. The seminar series is open and free to everyone in the Stanford community, as well as anyone from the surrounding community, companies or institutions. Our next seminar will host Dr. Jennifer Doudna, Professor of Chemistry, Biochemistry & Molecular Biology, &Li Ka Shing Chancellor’s Professor in Biomedical and Health, University of California, Berkeley; for her presentation on the “World of CRISPR: Editing Genomes and Altering Our Future”.
More Information: http://med.stanford.edu/radiology/imagining-the-future.html
Register: https://www.onlineregistrationcenter.com/JenniferDoudna
Integrative Biomedical Imaging Informatics at Stanford (IBIIS) and Center for Artificial Intelligence in Medicine & Imaging (AIMI) Seminar: “AI-Aided Diagnostic and Prognostic Tools for Prostate Cancer”
Okyaz Eminaga, MD, PhD
Postdoctoral Research Fellow, Urology
Biomedical Data Sciences
Stanford University
James H. Clark Center, S360
12:00pm-1:00pm – Seminar and Discussion (light refreshments provided)
Join via Zoom: https://stanford.zoom.us/j/613898274
ABSTRACT: Prostate Cancer exhibits different clinical behavior, ranging from indolent to lethal disease. A critical clinical need is identifying characteristics that distinguish indolent from advanced disease to direct treatment to the latter. The recent renaissance of artificial intelligence (AI) research uncovered the potential of AI to improve clinical decision making. In this seminar, we will go through the potential of AI to enhance the diagnosis and the prognosis of prostate cancer using magnetic resonance images, clinical data, and histology images. We will stress the challenges and benefits of having such AI-based solutions in clinical routine.
ABOUT: Dr. Eminaga passed his medical examination (Staatsexamen) 2009 and received his Ph.D. in Medicine 2010 from University of Muenster (major topic: medical informatics) under the supervision of Professor Dr. Axel Semjonow (one of the pioneer physician-scientists and biomarker researcher who worked on the standardization of PSA measurement for prostate cancer which is used nowadays) and Professor Dr. Martin Dugas (who is the head of European Research Center for Information Systems and one of the most influential professors in medical informatics in Europe). For those who don’t know the institute of medical informatics in Muenster. The systematized Nomenclature of Human and Veterinary Medicine (SNOMED), which is now used worldwide in medical information systems, was initiated by this institute more than 30 years ago.
His doctoral dissertation presented a novel documentation architecture for clinical data and imaging called cMDX (clinical map document) that facilitates the concept of the single-source information system for clinical data storage and analysis, and is successfully used in clinical routine for generating the pathology reports with graphical information about the spatial tumor extent for prostatectomy specimens since 2009 at the prostate center of University Hospital Muenster. This work has been also utilized for more than 20 studies related to genomics, translational medicine, epidemiology, urology, radiology, and pathology. Dr. Eminaga also established the biobanking information management system to manage the samples of one of the largest biobanks for prostate cancer in Europe. This biobank is also part of the European P-Mark network for prostate cancer-related biorepositories initiated by Oxford University.
Dr. Eminaga completed his residency in Urology in the University Hospital of Cologne (Germany) with a major focus on uro-oncology. He was also a research fellow in Prostate Center of University Hospital Muenster, doing research in biomarkers, biobanking infrastructure, epidemiology and histopathology. During his residency fellowship, he further evaluated the role of certain miRNA in prostate cancer development under the supervision of the molecular biologist Dr. Warnecke-Eberz. After his residency, he started a research scholarship at the laboratory of Dr. Brooks, doing genomic research and bioinformatics for research topics related to prostate cancer evolution. Now, his current interests have expanded to statistical learning, medical imaging informatics, and integrative data analysis.
He is the recipient of 3 highly-competitive scholarships and his works have been recognized at national and international levels e.g., by the European Association of Urology. Currently, he is an early-investigator research awardee for prostate cancer managed by the department of defense and works on developing decision-aided tools for diagnosis and prognosis of prostate cancer.
Follow us on Twitter: @StanfordIBIIS and @StanfordAIMI
http://ibiis.stanford.edu/events/seminars/2019seminars.html
“Messaging in the Age of Microtargeting”
John Stafford
Assistant Vice President
Digital Strategy
Stanford University
Bjorn Carey
Senior Director
Digital Strategy
Stanford University
Join via Zoom: https://stanford.zoom.us/j/400566542
Abstract:
Communications has become increasingly data-driven, targeted, and personalized. This has changed how Stanford analyzes communications opportunities from a research perspective and how it engages with relevant audiences. In this presentation, John and Bjorn will share the data and communications strategy underlying three communications initiatives and the resulting execution. They will also provide practical advice for individual thought leadership and communications in this dynamic environment.
About:
John Stafford, MA ’06, is currently Assistant Vice President for Digital Strategy at Stanford, the most senior digital communications role in the university. John is responsible for all aspects of creating a world-class digital communications function: setting the group’s strategy, building analytics and insight programs, counseling on crisis communications, leading multi-channel messaging initiatives, and advising colleagues across the University. He received a Master’s Degree in Communication from Stanford, a B.A. in History from the University of San Francisco, and was a founding advisor to Stanford Medicine X.
Refreshments will be provided.
“Algorithm Development Lifecycle in Medical Imaging:
Current State and Considerations for the Future”
Luciano M. Prevedello, MD, MPH
Vice-Chair for Medical Informatics and Augmented Intelligence in Imaging
Division Chief, Medical Imaging Informatics
Director, 3D and Advanced Visualization Lab
Associate Professor, Division of Neuroradiology,
Department of Radiology
Ohio State University Wexner Medical Center
Join via Zoom: https://stanford.zoom.us/j/267814863
Abstract:
This presentation will describe some of the most important considerations involved in creating algorithms in medical imaging from inception to deployment as well as continued model improvement and/or monitoring. Examples of experience to date from the OSU laboratory for augmented intelligence in imaging will be provided. New paradigms in model creation and the role of image challenge competitions will also be covered. Current issues with model validation and generalizability will also be introduced as well as considerations for future work in this area.
Refreshments will be provided.
“A Deep Learning Framework for Efficient Registration of MRI and Histopathology Images of the Prostate”
Wei Shao, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University
“Applications of Generative Adversarial Networks (GANs) in Medical Imaging”
Saeed Seyyedi, PhD
Paustenbach Research Fellow
Department of Radiology
Stanford University
Join via Zoom: https://stanford.zoom.us/j/593016899
Refreshments will be provided
ABSTRACT (Shao)
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, MRI interpretation suffers from high interobserver variability and often misses clinically significant cancers. Registration of histopathology images from patients who have undergone surgical resection of the prostate onto pre-operative MRI images allows direct mapping of cancer location onto MR images. This is essential for the discovery and validation of novel prostate cancer signatures on MRI. Traditional registration approaches can be computationally expensive and require a careful choice of registration hyperparameters. We present a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of preprocessing, transform estimation by deep neural networks, and postprocessing. We refined the registration neural networks, originally trained with 19,642 natural images, by adding 17,821 medical images of the prostate to the training set. The pipeline was evaluated using 99 prostate cancer patients. The addition of the images to the training set significantly (p < 0.001) improved the Dice coefficient and reduced the Hausdorff distance. Our pipeline also achieved comparable accuracy to an existing state-of-the-art algorithm while reducing the computation time from 4.4 minutes to less than 2 seconds.
ABSTRACT (Seyyedi)
Generative adversarial networks (GANs) are advanced types of neural networks where two networks are trained simultaneously to perform two tasks of generation and discrimination. GANs have gained a lot of attention to tackle well known and challenging problems in computer vision applications including medical image analysis tasks such as medical image de-noising, detection and classification, segmentation and reconstruction.In this talk, we will introduce some of the recent advancements of GANs in medical imaging applications and will discuss the recent developments of GAN models to resolve real world imaging challenges.