Mammography with deep learning for breast cancer detection

Area of Science:

• Radiology
• Artificial Intelligence
• Oncology

Background:

• X-ray mammography is the standard for breast cancer screening but has limitations in accuracy.
• Deep learning (DL) offers potential for personalized mammography, enhancing risk assessment and treatment planning.

Purpose of the Study:

• To review recent advancements in deep learning-based mammography for breast cancer detection and classification.
• To highlight the potential of DL-assisted mammography in improving screening accuracy.

Main Methods:

• Literature review of deep learning applications in mammography.
• Analysis of studies focusing on breast cancer detection and classification using AI.

Main Results:

• Deep learning models show promise in improving the accuracy of breast cancer screening.
• Customized mammography through DL can provide more precise patient information.

Conclusions:

• Deep learning-assisted mammography has significant potential to enhance breast cancer screening accuracy.
• Challenges in clinical implementation, including data privacy and model interpretability, require further research for successful integration.