Weiss Romano, Karimijafarbigloo Sanaz, Roggenbuck Dirk, Rödiger Stefan
Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Universitätsplatz 1, D-01968 Senftenberg, Germany.
Faculty of Health Sciences Brandenburg, Brandenburg University of Technology Cottbus-Senftenberg, D-01968 Senftenberg, Germany.
Biomedicines. 2022 Jun 21;10(7):1469. doi: 10.3390/biomedicines10071469.
Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.
用于深度学习应用的神经网络，也称为人工神经网络，是科学和工业领域的重要工具。虽然过去由于硬件不足，它们的广泛应用受到限制，但从21世纪初开始，当能够训练越来越大且复杂的网络时，它们的受欢迎程度急剧上升。如今，深度学习在生物医学中得到广泛应用，从图像分析到诊断。这也包括一些特殊领域，如法医学。在这篇综述中，我们讨论了最新的网络及其工作方式，重点是生物医学数据的分析，特别是生物图像数据中的生物标志物。我们对许多技术方面进行了总结，如激活函数和框架。我们还对关于神经网络的出版物进行了数据分析，以定量洞察网络类型的使用情况以及每年的期刊数量，从而确定在不同科学领域的应用情况。
