Deep Learning in Medical Imaging: General Overview

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning
• Medical Imaging

Background:

• Artificial Neural Networks (ANNs), inspired by human neuronal systems, emerged in the 1950s but faced limitations.
• Previous challenges included vanishing gradients, overfitting, insufficient computing power, and lack of data.
• Renewed interest is driven by big data availability, powerful graphics processing units (GPUs), and advanced deep learning algorithms.

Purpose of the Study:

• To review the history and development of deep learning technology.
• To explore the current and future applications of deep learning, with a focus on medical imaging.
• To provide perspectives on the evolution and potential of ANNs in healthcare.

Main Methods:

• Review of historical development of artificial neural networks.
• Analysis of factors enabling recent advancements in deep learning.
• Exploration of current research and potential applications in medical imaging.

Main Results:

• Deep learning models show potential to surpass human performance in visual and auditory recognition tasks.
• Advancements in computing power and data availability have overcome previous limitations of ANNs.
• Significant potential for AI applications in medicine and healthcare, particularly in medical imaging analysis.

Conclusions:

• Deep learning technology has evolved significantly, overcoming earlier constraints.
• The resurgence of ANNs is poised to revolutionize medical imaging and other healthcare sectors.
• Future applications of deep learning in medical diagnostics and treatment are highly promising.