Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2872-2875,2880

Pulmonary nodule detection via hybrid loss based joint fine-tuning and multi-scale classification

Yao Yujin
Zhang Li
Dept. of Electronic Engineering, Tsinghua University, Beijing 100084, China
DOI: 10.19734/j.issn.1001-3695.2018.03.0153

Abstract

To solve the problem that lung nodule automatic detection methods for CT images can only get low sensitivities with a lot of false positives, this paper proposed an integrated method with hybrid loss based 3D fully convolutional network as candidate detection and attention-based multi-scale residual network as nodule classification. Firstly, this paper established a 3D fully convolutional network based on dice coefficient loss, and the network filtered hard negative samples uniting with positives to fine-tune. Then, it designed an attention based multi-scale 3D residual convolutional network to classify the candidate and recognize true nodules. Experiment results on the LUNA16 dataset show that the proposed method achieves the sensitivity of 97.18% at 59.1 false positives per scan in the candidate detection stage, and the whole system achieves the average sensitivity of 0.880, which demonstrates this proposed method can improve sensitivity with fewer false positives and achieve superior performance.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0153
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Technology of Graphic & Image
Pages: 2872-2875,2880
Serial Number: 1001-3695(2019)09-067-2872-04

Publish History

[2019-09-05] Printed Article

Cite This Article

姚宇瑾, 张利. 基于混合损失联合调优与多尺度分类相结合的肺结节检测算法 [J]. 计算机应用研究, 2019, 36 (9): 2872-2875,2880. (Yao Yujin, Zhang Li. Pulmonary nodule detection via hybrid loss based joint fine-tuning and multi-scale classification [J]. Application Research of Computers, 2019, 36 (9): 2872-2875,2880. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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