论文标题
3D对医学图像的深入学习：评论
3D Deep Learning on Medical Images: A Review
论文作者
论文摘要
机器学习，图形处理技术和医学成像数据的可用性的快速进步导致医学领域中深度学习模型的使用迅速增加。基于卷积神经网络（CNN）建筑的快速进步，医学成像界采用的卷积神经网络（CNN）的快速进步加剧了这一点。自从Alexnet在2012年取得巨大成功以来，CNN越来越多地用于医学图像分析中，以提高人类临床医生的效率。近年来，已经使用了三维（3D）CNN进行医学图像分析。在本文中，我们追踪了如何从机器学习根中开发3D CNN的历史，我们提供了3D CNN的简要数学描述，并提供了医学图像所需的预处理步骤，然后再将其喂入3D CNN。我们在不同医学领域（例如分类，分割，检测和本地化）中使用3D CNN（及其变体）进行了3D医学成像分析领域的重要研究。最后，我们讨论了与在医学成像领域中使用3D CNN相关的挑战（以及一般的深度学习模型）以及该领域可能的未来趋势。
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
