Skip to content
Dr. Ram KumarDiabetes & metabolic health

Medical AI · Track 3 of 10

Deep learning & imaging

Neural networks for images and signals: classification, segmentation, ECG and time-series models, and imaging foundation models.

Some Python and the Machine learning track recommended first.

By the end of this track you should be able to

  • Explain how a neural network learns, in plain terms and in code
  • Train an image classifier and a segmentation model on open medical images
  • Model ECG and other physiological time series
  • Use transfer learning and judge when a foundation model helps

Lessons

  1. Neural-network basics

    Coming soon

  2. CNNs for chest X-ray and retinal images

    Coming soon

  3. Image segmentation with U-Net

    Coming soon

  4. ECG and physiological time-series models

    Coming soon

  5. Transfer learning and imaging foundation models

    Coming soon

Lessons in this track are being written and reviewed. In the meantime, start with the Foundations track.

Next track: Clinical NLP

Educational content only. This material teaches methods; it is not clinical advice. Code, models and examples here are not validated medical devices and must not be used to make decisions about real patients without proper clinical validation, governance and any required regulatory approval. Examples use synthetic or openly licensed de-identified data only. Never paste identifiable patient information into public AI tools. Mentions of models, tools or vendors are for identification, not endorsement.