AWS Certified Cloud Practitioner
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A healthcare analytics team has trained an AI model to analyze X-ray images. Now, they want to adapt it to detect MRI-based anomalies without retraining from scratch. Which learning technique is most suitable?
Real Exam
Community
RRitesh
0
A
Reinforcement Learning
B
Transfer Learning
C
Few-shot prompting
D
Zero-shot inference
Explanation:
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.
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