Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Science

Background:

• Medical imaging is crucial for disease diagnosis, but expert analysis is costly.
• Advanced imaging aids early, non-invasive disease detection.
• Deep Learning (DL) shows promise for medical image analysis but requires extensive data and computational resources.

Purpose of the Study:

• To review Deep Transfer Learning (DTL) methods for medical imaging.
• To explore DTL's role in overcoming DL limitations like high training costs and data dependency.
• To cover DTL concepts, modalities, tasks, techniques, datasets, and recent research trends.

Main Methods:

• Review of Deep Transfer Learning (DTL) techniques applied to medical imaging.
• Analysis of DTL integration with Deep Learning (DL) and Federated Learning (FL).
• Categorization of research over the past seven years by anatomical area.

Main Results:

• DTL effectively leverages knowledge from source tasks to target tasks, reducing data needs.
• Network-based DTL strategies, including fine-tuning and federated learning, enhance diagnostic accuracy.
• DTL improves robustness and generalization across various medical imaging modalities, especially in data-limited settings.

Conclusions:

• Deep Transfer Learning (DTL) offers a powerful solution to the challenges of DL in medical imaging.
• DTL, particularly with federated learning, significantly boosts diagnostic performance and efficiency.
• Future research should focus on further advancements and addressing challenges in DTL for medical imaging.