生物医学工程学杂志

生物医学工程学杂志

基于深度学习和医学图像的癌症计算机辅助诊断研究进展

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日益精细化的癌症医学图像提供了大量的有用信息,对辅助医生作出准确诊断发挥着至关重要的作用。为了准确、高效地利用这些信息,基于癌症医学图像的计算机辅助诊断(CAD)研究成为业界热点。近年来,深度学习技术的应用使这方面的研究取得了长足进步。本文拟就深度学习应用于癌症医学图像的计算机辅助诊断的研究进展予以综述。我们发现深度学习在肿瘤分割和分类方面展示了比传统浅层学习方法更好的效果,不仅有广阔的研究空间,也有较好的临床应用前景。

The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.

关键词: 癌症; 医学图像; 深度学习; 计算机辅助诊断; 肿瘤分割; 肿瘤分类

Key words: cancer; medical images; deep learning; computer-aided diagnosis; tumor segmentation; tumor classification

引用本文: 陈诗慧, 刘维湘, 秦璟, 陈亮亮, 宾果, 周煜翔, 汪天富, 黄炳升. 基于深度学习和医学图像的癌症计算机辅助诊断研究进展. 生物医学工程学杂志, 2017, 34(2): 314-319. doi: 10.7507/1001-5515.201609047 复制

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