Full course coming soon

Medical Imaging Data for AI: Acquisition, Preparation, and Validation

7 chapters · 34 lessons

1. 1. Clinical Imaging Modalities and Data Characteristics

   5 lessons

   Understand how CT, MRI, X-ray, and ultrasound produce structured clinical measurements with distinct properties.

2. 2. Preprocessing Clinical Scans for Machine Learning

   5 lessons

   Transform raw DICOM data into normalized tensors while preserving clinical meaning.

3. 3. Scanner Variability and Acquisition Artifacts

   5 lessons

   Recognize and address technical variation that affects model generalization.

4. 4. Clinical Annotation and Ground Truth Challenges

   5 lessons

   Understand how radiologist labels are created, what they represent, and where they fail.

5. 5. Domain Native Pretraining and Transfer Learning

   5 lessons

   Design pretraining strategies that leverage medical imaging structure and clinical context.

6. 6. Dataset Composition and Distribution Shift

   4 lessons

   Build training sets that reflect deployment environments and avoid hidden biases.

7. 7. Validation, Regulatory Evidence, and Clinical Integration

   5 lessons

   Design validation studies that meet clinical and regulatory standards for imaging AI.