Clinical Machine Learning
You will train, break, and honestly evaluate a deep-learning model on real medical data — images, signals, patient timelines, or -omics — and produce the deployment case that says what it is and is not allowed to do.
14 lessons · 11.5 hours
Who it's for. Clinicians, researchers, and engineers who have already built and validated a tabular clinical model and now face data that does not fit in a spreadsheet: radiology and pathology images, ECG and monitoring waveforms, longitudinal EHR sequences, and the -omics layers — genomics, transcriptomics, proteomics, radiomics, and their integration. It suits a radiologist evaluating a foundation model, a molecular-pathology researcher moving beyond differential expression, a genomics fellow asked whether a polygenic score is usable, and an engineer building the pipeline behind any of them.
The capstone. One deep-learning model on imaging, signal, sequence, or -omics data — with a shortcut audit, an external or scanner/batch-shift test, a golden-set evaluation of any generative component, and a deployment case including a silent-trial protocol.
Lessons open with membership. Join the network to work through this course at your own pace, keep your progress, and earn a certificate of completion when you finish.
Join the network