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Artificial Intelligence

Deep Learning for Medical Imaging

Dr Hiro Yamada · 24 hours · Advanced · ★ 4.6 (920 learners)

Learning outcomes

  • Describe convolutional and transformer architectures
  • Design a training pipeline for WSI patches
  • Evaluate generalisation across sites
  • Recognise common methodological failures

Curriculum

Module 1: Neural network basics
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 2: CNNs and vision transformers
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 3: Tiling and multiple instance learning
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 4: Training pipelines
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 5: External validation
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 6: Failure modes and reporting
Recorded lectures, reading list, worked examples and a short self-assessment.

Certification

Certificate with assessment. Awarded on successful completion of the end-of-course assessment.

Course certificates state clearly whether they represent participation, completion, assessment or CME credit. They do not constitute professional certification or a licence to practise.