*Canceled* Will Be Rescheduled.

Jayashree Kalpathy-Cramer, PhD

Chief of Artificial Medical Intelligence Division
Department of Ophthalmology
University of Colorado

Title: Opportunities and Challenges in Medical Imaging AI:
Lessons from Radiology, Oncology and Ophthalmology

Artificial intelligence and machine learning have the potential to greatly transform healthcare. We will begin with highlighting a few applications in radiology, oncology and ophthalmology. Although these techniques have shown remarkable performance for many tasks including medical image analysis, we will share some of the challenges that we have faced in developing robust and trustworthy algorithms including a lack of repeatability, explainability, generalizability, and the potential for bias. We will conclude with a some mitigating strategies and recommendations.

Wednesday, March 20, 2024
12:00 - 1:00pm (PT)
Clark Center - S360
Zoom Link

Andrey Fedorov, PhD 

Associate Professor, Harvard Medical School
Lead Investigator, Brigham and Women's Hospital

Title: NCI Imaging Data Commons: Towards Transparency, Reproducibility, and Scalability in Imaging AI

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires  easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts over 50 TB of diverse publicly available cancer image data spanning radiology and microscopy domains. By harmonizing all  data based on industry standards and colocalizing it with analysis and exploration resources, IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and  transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected  by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and  cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. The presentation will discuss the recent developments in IDC, highlighting resources, demonstrations and examples that we hope can help you improve your everyday imaging research practices - both those that use public and internal datasets.

Wednesday, April 17, 2024
12:00 - 1:00pm (PT) 
Clark Center - S360

Roxana Daneshjou, MD, PhD

Clinical Scholar in Dermatology Biomedical Data Science
Stanford University

Wednesday, May 15, 2024
12:00 - 1:00pm (PT) 
Clark Center - S360

Mildred Cho, PhD

Professor of Pediatrics, Center of Biomedical Ethics
Professor of Medicine, Primary Care and Population Health
Stanford University

Wednesday, June 19, 2024
12:00 - 1:00pm (PT)
Virtual Zoom Seminar: Link TBA

Bo Wang, PhD

Lead Artificial Intelligence Scientist, Peter Munk Cardiac Centre and the Techna Institute, University Health Network (UHN)
Faculty of Medicine, University of Toronto
Canada CIFAR Artificial Intelligence Chair

Wednesday, October 16, 2024
12:00 - 1:00pm (PT)
Clark Center - S360

Ipek Oguz, PhD

Assistant Professor of Computer Science
Assistant Professor of Electrical and Computer Engineering
Vanderbilt University

Wednesday, November 20, 2024
12:00 - 1:00pm (PT)
Clark Center - S360

Hugo Aerts, PhD

Associate Professor, Brigham and Women's Hospital
Harvard Medical School, Dana-Farber Cancer Institute

Wednesday, December 11, 2024
12:00 - 1:00pm (PT)
Virtual Zoom Seminar: Link TBA

Evis Sala, PhD

Professor of Oncological Imaging
University of Cambridge