Research progress in deep learning-based digital mammography for accurate diagnosis of breast clustered microcalcification

VernacularTitle:基于深度学习的数字乳腺X线摄影在乳腺簇状微小钙化精准诊断中的研究进展
Author: Xuan YANG ¹ ; Aiping DONG ; Yunzhang CHENG ; Baosan HAN
Author Information

1. 上海理工大学健康科学与工程学院，上海　200093
Keywords: Mammography; Breast clustered microcalcification; Breast cancer; Artificial intelligence; Deep learning
From: International Journal of Biomedical Engineering 2024;47(5):497-503
CountryChina
Language:Chinese
Abstract: Breast clustered microcalcification (BCM) is one of the most critical X-ray signs of early breast cancer. However, due to the fact that BCM is very tiny and hidden, and manual interpretation is susceptible to subjective factors such as visual fatigue, manual diagnosis of dense BCM based on X-images suffers from a low detection rate, high false-negative rate, and high recall rate. In recent years, with the continuous optimization innovation of deep learning algorithms, and advancements of computer hardware technology, new hope has been brought to accurate diagnosis of BCM, which is expected to realize the accurate assessment of individual breast cancer risk. In this review, the research progress of deep learning for accurate diagnosis of BCM was summarized, such as full-field digital mammography (FFDM), digital breast tomosynthesis (DBT) and contrast-enhanced mammography (CEM), as well as the future developments in this field were discussed.