Transferring deep neural networks for the differentiation of mammographic breast lesions
文献类型：期刊论文
| 作者 | Dai Guangzhe; Xie Yaoqin; Yu Shaode; Liu Lingling; Wang Zhaoyang |
| 刊名 | SCIENCE CHINA-TECHNOLOGICAL SCIENCES
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Machine learning can help differentiating benign and malignant lesions seen on mammographic images. Conventional models require handcrafting features for lesion representation. Due to insufficient medical instances, the performance of convolutional neural networks (CNNs) can be further increased. This study makes use of transfer learning for mammographic breast lesion diagnosis and deep neural network (DNN) models pre-trained with large-scale natural images are employed. The diagnosis performance is evaluated with the prediction accuracy (ACC) and the area under the curve (AUC) on average. A histologically verified database is analyzed which contains 406 lesions (230 benign and 176 malignant). Involved models include transferred DNNs (GoogLeNet and AlexNet), shallow CNNs (CNN2 and CNN3) that are fully trained with medical instances and boosted by support vector machine (SVM), and two conventional methods which combine handcrafted features and SVM for lesion diagnosis. Experimental results indicate that GoogLeNet achieves the best performance (ACC=0.81, AUC=0.88), followed by AlexNet (ACC=0.79, AUC=0.83) and CNN3 (ACC=0.73, AUC=0.82). Knowledge transfer can improve the mammographic breast cancer diagnosis, while its wide application still requires further verification in medical imaging domain. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14256]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Dai Guangzhe,Xie Yaoqin,Yu Shaode,et al. Transferring deep neural networks for the differentiation of mammographic breast lesions[J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES,2018. |
| APA | Dai Guangzhe,Xie Yaoqin,Yu Shaode,Liu Lingling,&Wang Zhaoyang.(2018).Transferring deep neural networks for the differentiation of mammographic breast lesions.SCIENCE CHINA-TECHNOLOGICAL SCIENCES. |
| MLA | Dai Guangzhe,et al."Transferring deep neural networks for the differentiation of mammographic breast lesions".SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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