Deep learning in medical imaging and radiation therapy

Area of Science:

• Medical Imaging
• Radiation Therapy
• Deep Learning

Background:

• Deep learning (DL) is increasingly applied in medical imaging and radiation therapy.
• Understanding current achievements and challenges is crucial for future development.

Purpose of the Study:

• To review deep learning applications in medical imaging and radiation therapy.
• To identify challenges and successful strategies.
• To highlight future research directions and technical innovations.

Main Methods:

• Introduction to deep learning and convolutional neural networks.
• Survey of five major application areas.
• Identification of common themes and dataset expansion methods.

Main Results:

• Summary of current achievements in DL for medical imaging and radiation therapy.
• Identification of common and unique challenges faced by researchers.
• Discussion of strategies employed to overcome these challenges.

Conclusions:

• Deep learning shows significant promise in medical imaging and radiation therapy.
• Addressing dataset limitations and technical challenges is key for future progress.
• Future directions include novel applications and technical advancements.