Duration: 6 Weeks
Participants will learn about the relationship between a clinical problem and a machine learning problem as well as the influence of training data quality and annotation errors on the ability of algorithms to learn. Participants will be introduced to state-of-the-art AI models and learn how to apply and validate them in practice.
Understand the challenges and practicalities of assembling a training set of medical images
Understand the tradeoffs between manual or automatic labelling of training data
Understand how to select a state-of the art model, and have hands-on experience training such a model
Understand and have hands on experience with selecting and applying appropriate metrics for the performance of a medical AI system
Understand how the results of validating a machine-learning model may affect design choices in a subsequent experiment