“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.
CEDSS: “Strategies to Identify Aggressive Breast Cancer Biology in Black and Latina Women”
Victoria Seewaldt, MD
Ruth Ziegler Professor and Chair, Department of Population Sciences
Associate Director for Population Sciences Research, Comprehensive Cancer Center
City of Hope
Beckman Center, Munzer Auditorium (B060)
11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception & Light Refreshments
RSVP here: https://www.onlineregistrationcenter.com/VictoriaSeewaldt
ABSTRACT
Over 90% of breast cancer is cured; yet there remain highly aggressive breast cancers that develop rapidly and are extremely difficult to treat, much less prevent. Examples are triple-negative breast cancer in Black/African American women and luminal B breast cancers in Black/African Americans and Latinas. Breast cancers that rapidly develop between breast imaging are called “interval cancers”. Here we aim to investigate biologically aggressive precancerous breast lesions and their matched invasive breast cancers in women of diverse race and ethnicity. Our team has the unique ability to perform single cell in situ transcriptional profiling in combination with dynamic and spatial genomics/proteomics; this allows us to identify multi-dimensional spatial and temporal relationships that drive the transition from biologically aggressive pre-cancer to interval breast cancer.
ABOUT
Victoria Seewaldt, M.D., is an accomplished clinician and researcher who’s devoted to improving the lives of her patients and the community at large. She has led community outreach education efforts on cancer prevention through personal wellbeing and directed research aimed at finding biomarkers that can be used for early cancer detection, particularly triple-negative breast cancers that are especially resistant to treatment.
At City of Hope, Dr. Seewaldt will direct efforts to provide breast cancer education, free breast cancer screening and treatment, mentorship of young minority scholars, and a forum for community partnered trials. Clinically, Dr. Seewaldt aims to empower women at high breast cancer risk to be full partners in developing wellness strategies to promote personal health.
Dr. Seewaldt received her medical degree from the University of California, Davis, and completed her residency and clinical fellowship at the University of Washington in Seattle. She then pursued a medical oncology fellowship with the Fred Hutchinson Cancer Research Center and then became an assistant professor at Ohio State University. Afterwards, she transferred to Duke University, where she held various clinical, academic and leadership roles in its School of Medicine and Comprehensive Cancer Center — most recently as a professor, co-leader of the breast and ovarian cancer program and head of the cancer breast prevention program — before joining City of Hope.
Please note this seminar is now cancelled and will be rescheduled for a future date. Please contact Ashley Williams (ashleylw@stanford.edu) with any questions or concerns. Thank you for your understanding!
CEDSS: “The First Cell and the Human Cost of going after Cancer’s last”
Chan Soon-Shiong Professor of Medicine
Director, Myelodysplastic Syndrome Center
Columbia University Medical Center
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.
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
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
Cancer Research UK, OHSU Knight Cancer Institute and the Canary Center at Stanford, present the Early Detection of Cancer Conference series. The annual Conference brings together experts in early detection from multiple disciplines to share ground breaking research and progress in the field.
The Conference is part of a long-term commitment to invest in early detection research, to understand the biology behind early stage cancers, find new detection and screening methods, and enhance uptake and accuracy of screening.
The 2020 conference will take place October 6-8 virtually.
Cancer Research UK, OHSU Knight Cancer Institute and the Canary Center at Stanford, have been closely monitoring developments relating to the coronavirus (COVID-19) outbreak and reviewing guidance from government bodies. After careful consideration, we have made the decision to convert the Early Detection of Cancer Conference 2020 to a virtual conference, instead of the scheduled in-person conference on October 6-8 in London, UK.
For more information visit the website: http://earlydetectionresearch.com/
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.