Answer: Transfer Learning

## Explanation **Transfer Learning** is the most suitable technique for this scenario because: - **Transfer Learning** allows you to take a pre-trained model and adapt it to a new but related task without starting from scratch - The model already has learned features from X-ray images that can be leveraged for MRI analysis (both are medical imaging domains) - This approach saves significant computational resources and time compared to full retraining - Only the final layers typically need to be fine-tuned for the new MRI-based anomaly detection task **Why other options are not suitable:** - **Reinforcement Learning (A)**: Involves learning through trial-and-error interactions with an environment, not adapting existing models - **Few-shot prompting (C)**: Typically used in language models with limited examples, not ideal for adapting computer vision models - **Zero-shot inference (D)**: Works without any training examples, but here the model needs adaptation to a new domain (MRI vs X-ray) Transfer Learning is particularly effective when the source and target domains are related, as in this medical imaging case.

Author: Ritesh Yadav