| Literature DB >> 29745591 |
Shihui Chen1, Weixiang Liu1, Jing Qin2, Liangliang Chen1, Guo Bin1, Yuxiang Zhou1, Tianfu Wang1, Bingsheng Huang1.
Abstract
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.Entities:
Keywords: cancer; computer-aided diagnosis; deep learning; medical images; tumor classification; tumor segmentation
Year: 2017 PMID: 29745591 DOI: 10.7507/1001-5515.201609047
Source DB: PubMed Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ISSN: 1001-5515