论文标题
深度学习的趋势，用于医学高光谱图像分析
Trends in deep learning for medical hyperspectral image analysis
论文作者
论文摘要
在过去的十年中，深度学习算法在整个感兴趣的几个领域中都在其应用中急剧增长，医学高光谱成像是一个特别有希望的领域。到目前为止，据我们所知，尚无评论论文讨论对医学高光谱成像的深度学习的实施，这是本评论论文旨在通过检查目前利用深度学习来对医学高光谱成像进行有效分析的出版物来实现的目标。本文讨论了与医学高光谱成像分析相关且适用的深度学习概念，这些概念自深度学习繁荣以来就已经实施了一些。这将包括审查对分类，分割和检测的深度学习的使用，以研究医学高光谱成像的分析。最后，我们讨论了与该学科有关的当前和未来挑战以及克服此类试验的可能努力。
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this review paper aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.
