Part 5/11:

1. Preprocessing: Techniques like active contours are used to refine and shrink annotations, reducing noise and focusing the model's attention on relevant areas.

2. Model Training: Using the preprocessed, annotated data, models learn to recognize patterns associated with different pathologies, effectively "learning" how to segment or detect features autonomously.

3. Inference and Validation: The trained models are tested on new, unseen images to evaluate how accurately they can detect and segment abnormal regions. Metrics like Dice Score and Intersection over Union (IoU) quantify the degree of overlap between AI predictions and expert annotations.