卷积神经网络
计算机科学
预处理器
人工智能
偏移量(计算机科学)
机器学习
卷积(计算机科学)
医学影像学
深度学习
人工神经网络
程序设计语言
作者
Yashbir Singh,Colleen M. Farrelly,Quincy A. Hathaway,Ashok Choudhary,Gunnar Carlsson,Bradley J. Erickson,Tim Leiner
标识
DOI:10.1016/j.mcpdig.2023.08.006
摘要
Convolutional neural networks (CNNs) have played an important role in medical imaging—from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance all pose challenges for CNN training and accuracy; in addition, training costs can be high when many types of CNNs are needed in a health care system. Topology and geometry provide tools to ameliorate these challenges for CNNs when they are integrated into the CNN architecture, particularly in the data preprocessing steps or convolution layers. This paper reviews the current integration of geometric tools within CNN architectures to reduce the burden of large training datasets and offset computational costs. This paper also identifies fertile areas for future research into the integration of geometric tools with CNNs.
科研通智能强力驱动
Strongly Powered by AbleSci AI