Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354.
Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354.
Semin Vasc Surg. 2023 Sep;36(3):413-418. doi: 10.1053/j.semvascsurg.2023.07.001. Epub 2023 Jul 7.
Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-learn" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.
深度学习是人工智能领域中的机器学习的一个分支，在血管外科学中的医学图像分析中已取得成功。与传统的基于计算机的分割方法不同，后者从输入图像中手动提取特征，深度学习方法无需预先做出假设，即可学习图像特征并对数据进行分类。卷积神经网络是计算机视觉处理的主要深度学习类型，是具有多层次结构和节点间加权连接的神经网络，可以通过重复暴露于训练数据来“自动学习”，而无需人工输入或监督。这些网络在血管外科学成像分析中有许多应用，特别是在疾病分类、目标识别、语义分割和实例分割方面。本文的目的是回顾机器学习图像分析的相关概念及其在血管外科学领域的应用。
