Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study.
Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study.
复制标题
基于深度卷积神经网络的肝脏局灶性病变自动检测与分类：初步研究
DOI：
10.3389/fonc.2020.581210
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发表时间：
2020
影响因子：
4.7
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Zhou J;Wang W;Lei B;Ge W;Huang Y;Zhang L;Yan Y;Zhou D;Ding Y;Wu J;Wang W
With the increasing daily workload of physicians, computer-aided diagnosis (CAD) systems based on deep learning play an increasingly important role in pattern recognition of diagnostic medical images. In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of focal liver lesions (FLLs) in multi-phasic computed tomography (CT). A total of 616 nodules, composed of three types of malignant lesions (hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastasis) and benign lesions (hemangioma, focal nodular hyperplasia, and cyst), were randomly divided into training and test sets at an approximate ratio of 3:1. To evaluate the performance of our model, other commonly adopted CNN models and two physicians were included for comparison. Our model achieved the best results to detect FLLs, with an average test precision of 82.8%, recall of 93.4%, and F1-score of 87.8%. Our model initially classified FLLs into malignant and benign and then classified them into more detailed classes. For the binary and six-class classification, our model achieved average accuracy results of 82.5 and73.4%, respectively, which were better than the other three classification neural networks. Interestingly, the classification performance of the model was placed between a junior physician and a senior physician. Overall, this preliminary study demonstrates that our proposed multi-modality and multi-scale CNN structure can locate and classify FLLs accurately in a limited dataset, and would help inexperienced physicians to reach a diagnosis in clinical practice.
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影响因子：
5.9
作者：
Hamm, Charlie A.;Wang, Clinton J.;Letzen, Brian
通讯作者：
Letzen, Brian
影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
19.7
作者：
Nino-Murcia, M;Olcott, EW;Jain, KA
通讯作者：
Jain, KA
DOI：
10.1158/1078-0432.ccr-18-1115
发表时间：
2018-12-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Aboutalib SS;Mohamed AA;Berg WA;Zuley ML;Sumkin JH;Wu S
通讯作者：
Wu S