![AIMI, IBIIS & RSL Special Seminar - John Stafford & Bjorn Carey @ Clark Center - S360](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/11/stafford-carey-300x300.jpg)
“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.
![AIMI & IBIIS Seminar - Luciano M. Prevedello, MD, MPH @ Clark Center - S360](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/12/luciano-prevedello-300x300.jpg)
“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.
![AIMI & IBIIS Seminar - Wei Shao, PhD & Saeed Seyyedi, PhD @ Clark Center - S360](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/01/shao-seyyedi-300x300.jpg)
“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.
![SCIT Seminar: Muna Aryal Rizal, PhD and Eduardo Somoza, MD @ Glazer Learning Center (Lucas P083)](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/01/scit-300x300.jpg)
Muna Aryal Rizal, PhD
Mentor: Jeremy Dahl, PhD and Raag Airan, MD, PhD
Noninvasive Focused Ultrasound Accelerates Glymphatic Transport to Bypass the Blood-Brain Barrier
ABSTRACT
Recent advancement in neuroscience revealed that the Central Nervous System (CNS) comprise glial-cell driven lymphatic system and coined the term called “Glymphatic pathway” by Neuroscientist, Maiden Nedergaard. Furthermore, it has been proven in rodent and non-human primate studies that the glymphatic exchange efficacy can decay in healthy aging, alzheimer’s disease models, traumatic brain injury, cerebral hemorrhage, and stroke. Studies in rodents have also shown that the glymphatic function can accelerate by doing easily-implemented, interventions like physical exercise, changes in body posture during sleep, intake of omega-3 polyunsaturated fatty acids, and low dose alcohol (0.5 g/kg). Here, we proposed for the first time to accelerate the glymphatic function by manipulating the whole-brain ultrasonically using focused ultrasound, an emerging clinical technology that can noninvasively reach virtually throughout the brain. During this SCIT seminar, I will introduce the new ultrasonic approach to accelerates glymphatic transport and will share some preliminary findings.
Eduardo Somoza, MD
Mentor: Sandy Napel, PhD
Prediction of Clinical Outcomes in Diffuse Large B-Cell Lymphoma (DLBCL) Utilizing Radiomic Features Derived from Pretreatment Positron Emission Tomography (PET) Scan
ABSTRACT
Diffuse Large B-Cell lymphoma (DLBCL) is the most common type of lymphoma, accounting for a third of cases worldwide. Despite advancements in treatment, the five-year percent survival for this patient population is around sixty percent. This indicates a clinical need for being able to predict outcomes before the initiation of standard treatment. The approach we will be employing to address this need is the creation of a prognostic model from pretreatment clinical data of DLBCL patients seen at Stanford University Medical Center. In particular, there will be a focus on the derivation of radiomic features from pretreatment positron emission tomography (PET) scans as this has not been thoroughly investigated in similar published research efforts. We will layout the framework for our approach, with an emphasis on the aspects of our design that will allow for the translation of our efforts to multiple clinical settings. More importantly, we will discuss the importance and challenges of assembling a quality clinical database for this type of research. Ultimately, we hope our efforts will lead to the development of a prognostic model that can be utilized to guide treatment in DLBCL patients with refractory disease and/or high risk of relapse after completion of standard treatment.
![Evolving Health Care from an Artisanal Organization into an Industrial Enterprise @ Clark Center, S361](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/02/ron-kikinis-261x300.jpg)
Ron Kikinis, MD
Director of the Surgical Planning Laboratory
Professor of Radiology
Department of Radiology
Brigham and Women’s Hospital
Harvard Medical School
Title: Evolving Health Care from an Artisanal Organization into an Industrial Enterprise
Refreshments will be provided
Join via Zoom: https://stanford.zoom.us/j/996417088
Abstract: During the last decade, results from basic research in the fields of genetics and immunology have begun to impact treatment in a variety of diseases. Checkpoint therapy, for instance has fundamentally changed the treatment and survival of some patients with melanoma. The medical workplace has transformed from an artisanal organization into an industrial enterprise environment. Workflows in the clinic are increasingly standardized. Their timing and execution are monitored through omnipresent software systems. This has resulted in an acceleration of the pace of care delivery. Imaging and image post-processing have rapidly evolved as well, enabled by ever-increasing computational power, novel sensor systems and novel mathematical approaches. Organizing the data and making it findable and accessible is an ongoing challenge and is investigated through a variety of research efforts. These topics will be reviewed and discussed during the lecture.
About:
Dr. Kikinis is the founding Director of the Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, and a Professor of Radiology at Harvard Medical School. This laboratory was founded in 1990. Before joining Brigham & Women’s Hospital in 1988, he trained as a resident in radiology at the University Hospital in Zurich, and as a researcher in computer vision at the ETH in Zurich, Switzerland. He received his M.D. degree from the University of Zurich, Switzerland, in 1982. In 2004 he was appointed Professor of Radiology at Harvard Medical School. In 2009 he was the inaugural recipient of the MICCAI Society “Enduring Impact Award”. On February 24, 2010 he was appointed the Robert Greenes Distinguished Director of Biomedical Informatics in the Department of Radiology at Brigham and Women’s Hospital. On January 1, 2014, he was appointed “Institutsleiter” of Fraunhofer MEVIS and Professor of Medical Image Computing at the University of Bremen. Since then he is commuting every two months between Bremen and Boston.
During the mid-80’s, Dr. Kikinis developed a scientific interest in image processing algorithms and their use for extracting relevant information from medical imaging data. Due to the explosive increase of both the quantity and complexity of imaging data this area of research is of ever-increasing importance. Dr. Kikinis has led and has participated in research in different areas of science. His activities include technological research (segmentation, registration, visualization, high performance computing), software system development, and biomedical research in a variety of biomedical specialties. The majority of his research is interdisciplinary in nature and is conducted by multidisciplinary teams. The results of his research have been reported in a variety of peer-reviewed journal articles. He is an author and co-author of over 350 peer-reviewed articles.
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![Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples @ Clark Center S360](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/02/tessa-cook-300x300.jpg)
Tessa Cook, MD, PhD
Assistant Professor of Radiology
Perelman School of Medicine
University of Pennsylvania
Title: Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples
Abstract: Although many radiology AI efforts are focused on pixel-based tasks, there is great potential for AI to impact radiology care delivery and workflow when applied to reports, EMR data, and workflow data. Radiology-pathology correlation, identification of follow-up recommendations, and report segmentation can be used to increase meaningful feedback to radiologists as well as to automate tasks that are currently manual and time-consuming. When deploying AI within the clinical workflow, there are many challenges that may slow down or otherwise affect the integration. Careful consideration of the way in which radiologists may expect to interact with AI results should be undertaken to meaningfully deploy radiology AI in a safe and effective way.
![Mini-Grand Rounds - Aruna Subramanian, MD, and David Ha, PharmD @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/03/subramanian-ha-300x300.jpg)
Mini-Grand Rounds: Emerging treatment strategies for COVID-19
Aruna Subramanian, MD
Clinical Professor, Medicine
Stanford University
David Ha, PharmD
Infectious Disease Resident
Stanford University
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 - Payam Massaband, MD @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/03/payam-massaband-300x300.jpg)
Mini-Grand Rounds: Effective faculty-trainee workflow and education during the COVID-19 pandemic
Payam Massaband, MD
Clinical Associate Professor, Radiology
Stanford University
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 - Michele Barry, MD, FACP @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/03/michele-barry-300x300.jpg)
Mini-Grand Rounds: CoVID19: Lessons learned from the global response
Michele Barry, MD, FACP
Drs. Ben & A. Jess Shenson Professor
Senior Associate Dean, Global Health
Director, Center for Innovation in Global Health
Professor of Medicine & Senior Fellow at the Woods Institute and at the Freeman Spogli Institute
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 - Garry Gold, MD @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/03/garry-gold-300x300.jpg)
Mini-Grand Rounds: How to stay productive during the COVID-19 pandemic
Garry Gold, MD
Vice Chair, Research and Organization
Professor, Radiology
Stanford University
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.