Applying Deep Learning for Breast Cancer Detection in Radiology.
Ella MahoroMoulay A AkhloufiPublished in: Current oncology (Toronto, Ont.) (2022)
Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.
Keyphrases
- deep learning
- artificial intelligence
- big data
- machine learning
- convolutional neural network
- clinical practice
- high resolution
- healthcare
- breast cancer risk
- magnetic resonance imaging
- type diabetes
- electronic health record
- magnetic resonance
- childhood cancer
- papillary thyroid
- young adults
- photodynamic therapy
- insulin resistance
- skeletal muscle
- squamous cell
- real time pcr
- pregnancy outcomes