Workshop Title: Training Medical AI Models
Presenters: Yuka Kihara, Research Scientist, Yelena Bagdasarova, Research Scientist, Yue Wu, Acting Instructor, University of Washington School of Medicine, Lee Lab for Computational Ophthalmology
Abstract:
AI has the potential to transform healthcare and our understanding of medical diseases. This workshop will cover training AI models for medical and biological data from scratch, as well as transfer learning from foundational models. In addition, different types of AI training, supervised, semi-supervised and self-supervised, will be discussed with example use cases for each training type. The workshop aims to provide an overview of the challenges and opportunities in medical AI.
Hands-on: no
Takeaways:
- High level understanding of problems in medical AI
- Introduction to open datasets and challenges in medical AI
- An overview of some state-of-the-art methods in medical AI
Attendees:
- No Equipment and software needed.
- Prerequisite, None
- AI/ML light to medium
The certificate will be mailed to you upon completion of the workshop.
Presenter Bios:
Yuka Kihara, Research Scientist, University of Washington School of Medicine, Lee Lab for Computational Ophthalmology
Yuka Kihara is interested in the interface between machine learning, computer vision, and medical data analysis. She previously worked for a major Japanese imaging and electronics company developing new software functions for copiers, printers and projectors. She is a former visiting research scientist at Cornell University’s School of Electrical and Computer Engineering.
Yelena Bagdasarova, Research Scientist, University of Washington School of Medicine, Lee Lab for Computational Ophthalmology
Yelena Bagdasarova is currently using deep learning to segment Reticular Pseudodrusen lesions in retinal optical coherence tomography B-scans. She has a passion for learning and sharing information with others. Her scientific interests include Monte Carlo simulations, Data Analysis, and Deep Learning. She is part of Data Circles, a community of data scientists dedicated to creating a safe and welcoming environment for professional growth.
Yue Wu, Acting Instructor, University of Washington School of Medicine, Lee Lab for Computational Ophthalmology
Yue Wu is an Acting Instructor in the Department of Ophthalmology. He completed his BA in statistics at Harvard University and then earned his Ph.D. in engineering from the University of Cambridge. He joined the Department of Ophthalmology, working with Dr. Van Gelder and the Lee Lab in 2017. His scientific interests include statistics, probability, graphical models, Monte Carlo and Sequential Carlo methods, and deep learning.