Cancer AI Research: Computational Approaches Addressing Imperfect Data
This is an open workshop and free to attend, but registration is required.
Date: April 3-4, 2023, 11:00 AM-5:00 PM ET
Location: Virtual Meeting
Workshop Description: The application of AI to cancer research holds promise to accelerate new discoveries, enable early detection, improve diagnosis, and spur development of new therapies for cancer. Machine learning and other forms of AI have made a significant impact in some areas of cancer research, but the full promise of data-driven approaches has been elusive. While there are important ongoing efforts to collect and produce large, well-annotated datasets to support the training of robust deep learning models, the heterogeneity and complexity of cancer, along with privacy and bias concerns, continues to limit the application of AI methods to many critical areas of cancer research. There is a need for foundational advances in machine learning that can operate on incomplete, noisy, unbalanced and/or biased data across the cancer research continuum.
The goals of this workshop are to (1) examine the state of the science for AI methods designed to operate on noisy, complex, or low-dimensional data, (2) explore how these methods may be applied to key areas of cancer research, and (3) discuss processes for identifying the biological questions that will motivate further advances in machine learning. This workshop will highlight the importance of leveraging advances across fields to accelerate cancer research and discovery through AI.
Caroline Uhler, Ph.D. (MIT and Broad Institute)
Olivier Gevaert, Ph.D. (Stanford University)
NCI Planning Committee:
Juli Klemm, Ph.D.
Jennifer Couch, Ph.D.
Sean Hanlon, Ph.D.
Natalie Abrams, Ph.D.
Keyvan Farahani, Ph.D.
Emily Greenspan, Ph.D.
Paul Han, M.D., M.A., M.P.H.
Roxanne Jensen, Ph.D.
Jerry Li, M.D., Ph.D.