Literature DB >> 29745591

[Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging].

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


  3 in total

1.  [Value of ultrasonic S-Detect technique in diagnosis of breast masses].

Authors:  Y Cheng; Q Xia; J Wang; H Xie; Y Yu; H Liu; Z Yao; J Hu
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-07-20

2.  Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

Authors:  Zhi Wang; Yankui Chang; Zhao Peng; Yin Lv; Weijiong Shi; Fan Wang; Xi Pei; X George Xu
Journal:  J Appl Clin Med Phys       Date:  2020-11-25       Impact factor: 2.102

3.  Texture recognition of pulmonary nodules based on volume local direction ternary pattern.

Authors:  Zhipeng Fan; Huadong Sun; Cong Ren; Xiaowei Han; Zhijie Zhao
Journal:  Bioengineered       Date:  2020-12       Impact factor: 3.269

  3 in total

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