Deep Learning Applications in Breast Cancer Imaging: Past Achievements and Future Challenges
Subject Areas : Biomedical Engineering
Zahra Maghsoodzadeh Sarvestani
1
,
Salma Shirdel
2
1 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - IRIB University, Facultry of Media Engineering and Technology, Iran
Keywords: Mammograms, Ultrasound, Magnetic Resonance Imaging, Deep Learning,
Abstract :
Since 2020, breast cancer has become the most frequently diagnosed malignancy worldwide. The role of breast imaging in early detection and intervention is critical for improving patient outcomes. In the past decade, deep learning has revolutionized the analysis of breast cancer imaging, providing significant advancements in interpreting the complex data from various imaging modalities. With the rapid evolution of deep learning technology and the increasing incidence of breast cancer, it is essential to review past achievements and identify future challenges. This paper offers an extensive review of deep learning-based breast cancer imaging research, focusing on studies involving mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the last ten years. It highlights the primary deep learning methods and their applications in imaging-based screening, diagnosis, treatment response prediction, and prognosis. Based on the research findings, we discuss the challenges and propose potential future research directions in deep learning-based breast cancer imaging
#H. Sung et al., “Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,”CA: a cancer journal for clinicians, vol. 71, no. 3, pp. 209–249, 2021#
