乳腺磁共振深度学习的临床应用。
Clinical applications of deep learning in breast MRI.
发表日期：2023 Feb 21
作者：
Xue Zhao, Jing-Wen Bai, Qiu Guo, Ke Ren, Guo-Jun Zhang
来源：
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
摘要：
深度学习（DL）是人工智能（AI）中最强大的数据驱动机器学习技术之一。它能够自动从原始数据中学习，无需手动选择特征。DL模型在医学成像数据提取和分析方面已取得了显著进展。磁共振成像（MRI）已被证明对于描绘乳腺病变和肿瘤的特征和范围很有用。本综述总结了DL模型在乳腺MRI领域的最新应用现状。该领域最近的许多DL模型以及几种先进的学习方法和数据归一化和乳腺和病变分割方法都进行了研究。在临床应用方面，基于DL的乳腺MRI模型在五个方面证明了其有效性：乳腺癌的诊断、分子类型的分类、组织病理类型的分类、新辅助化疗反应的预测和淋巴结转移的预测。未来的研究需要进一步改进数据采集和预处理，探索更多乳腺MRI中的DL技术，以及广泛的临床应用。Copyright © 2023. Elsevier B.V.发表。
Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn from raw data without manual feature selection. DL models have led to remarkable advances in data extraction and analysis for medical imaging. Magnetic resonance imaging (MRI) has proven useful in delineating the characteristics and extent of breast lesions and tumors. This review summarizes the current state-of-the-art applications of DL models in breast MRI. Many recent DL models were examined in this field, along with several advanced learning approaches and methods for data normalization and breast and lesion segmentation. For clinical applications, DL-based breast MRI models were proven useful in five aspects: diagnosis of breast cancer, classification of molecular types, classification of histopathological types, prediction of neoadjuvant chemotherapy response, and prediction of lymph node metastasis. For subsequent studies, further improvement in data acquisition and preprocessing is necessary, additional DL techniques in breast MRI should be investigated, and wider clinical applications need to be explored.Copyright © 2023. Published by Elsevier B.V.