Hu T H, Wan L, Liu T A, Wang M W, Chen T, Wang Y H
Department of Forensic Science, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Fa Yi Xue Za Zhi. 2017 Dec;33(6):629-634. doi: 10.3969/j.issn.1004-5619.2017.06.013. Epub 2017 Dec 25.
Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment.
深度学习和神经网络模型近年来一直是机器学习和人工智能领域的新研究方向和热点问题。深度学习在图像和语音识别应用方面取得了突破，并且由于其特殊优势，也已广泛应用于人脸识别和信息检索领域。骨骼X射线图像呈现出黑白灰灰度的不同变化，具有黑白对比度和层次差异的图像特征。基于深度学习在图像识别方面的这些优势，我们将其与骨龄评估研究相结合，为构建法医骨龄评估自动系统提供基础数据。本文综述了深度学习的基本概念和网络架构，描述了其在国内外不同研究领域图像识别方面的最新研究进展，并探讨了其在骨龄评估中的优势和应用前景。
