Deep Learning: An Update for Radiologists.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Medical Imaging

Background:

• Deep learning methods, unlike traditional machine learning, automatically learn image features for classification.
• Convolutional neural networks (CNNs) are central to deep learning in imaging, utilizing multilayered artificial neural networks.
• These networks are increasingly vital in radiology for tasks like image classification and segmentation.

Purpose of the Study:

• To provide an updated primer on deep learning for radiologists.
• To review key concepts, trends, and applications of CNNs in medical imaging.
• To facilitate understanding and clinical adoption of deep learning techniques in radiology.

Main Methods:

• Review of deep learning terminology and data requirements.
• Illustration of CNN building blocks and architectures for computer vision tasks.
• Discussion of training, validation, performance metrics, and visualization techniques.

Main Results:

• An overview of recent trends in CNN design, including generative architectures.
• Explanation of essential concepts for understanding deep learning in medical imaging.
• Guidance on practical aspects such as training, validation, and performance evaluation.

Conclusions:

• Familiarity with deep learning concepts is essential for radiologists.
• Deep learning techniques hold significant potential for advancing medical imaging analysis.
• This review aims to support the clinical integration of deep learning in radiology.