Computer Science ›› 2017, Vol. 44 ›› Issue (8): 312-317.doi: 10.11896/j.issn.1002-137X.2017.08.054
Special Issue: Medical Imaging
Previous Articles Next Articles
ZHAO Xin, QIANG Yan and GE Lei
[1] LILLA B,LUYIN Z,LEE K P.Feature subset selection for improving the performance of false positive reduction in lung no-dule CAD[J].IEEE Transactions on Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine &Biology Society,2006,10(3):85-90. [2] LI Y,CHEN K Z,WANG J.Development and validation of a clinical prediction model to estimate the probability of malignancy in solitary pulmonary nodules in Chinese people[J].Clinical Lung Cancer,2011,12(5):313-319. [3] WEI Y,GUO W,SUN Y F,et al.A Lung Nodule Detection Al-gorithm Based on Local Maximum Gray Segmenting and Improved Mahalanob is Distance Classifying[J].Journal of Image and Graphics,2008,13(9):1720-1726.(in Chinese) 魏颖,郭薇,孙月芳,等.基于局部灰度最大和改进Mahalanobis距离分类的肺结节检测算法[J].中国图象图形学报,2008,13(9):1720-1726. [4] NIE S D,SUN X W,CHEN Z X.Progress on Computer-Aided Detection for Pulmonary Nodule Using CT Image[J].Chinese Journal of Medical Physics,2009,26(2):1075-1079.(in Chinese) 聂生东,孙希文,陈兆学.基于CT图像的肺结节计算机辅助检测技术的研究进展[J].中国医学物理学杂志,2009,26(2):1075-1079. [5] SUN S S,REN H Z,KANG Y,et al.Lung Nodule Detection by GA and SVM[J].Journal of System Simulation,2011,23(3):497-501.(in Chinese) 孙申申,任会之,康雁,等.基于遗传算法和支持向量机的肺结节检测[J].系统仿真学报,2011,23(3):497-501. [6] HUA K L,HSU C H,HIDAYATI S C,et al.Computer-aided classification of lung nodules on computed tomography images via deep learning technique[J].OncoTargets and therapy,2015,8:2015-2022. [7] SETIO A A,CIOMPI F,LITJENS G,et al.Pulmonary nodule detection in CT images:false positive reduction using multi-view convolutional networks.[J].IEEE Transactions on Medical Imaging,2016,35(5):1. [8] KUMAR D,WONG A,CLAUSI D A.Lung Nodule Classifica-tion Using Deep Features in CT Images[C]∥Computer and Robot Vision.IEEE,2015:110-116. [9] JIA T,ZHANG H,BAI Y K.Benign and Malignant Lung No-dule Classification Based on Deep Learning Feature[J].Journal of Medical Imaging & Health Informatics,2015,5(8):1936-1940. [10] WEI S,MU Z,FENG Y,et al.Multi-scale Convolutional Neural Networks for Lung Nodule Classification[M]∥Information Processing in Medical Imaging.Springer International Publi-shing,2015:588-99. [11] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma-chine:Theory and applications[J].Neurocomputing,2006,70(1-3):489-501. [12] HUANG G B,BAI Z,KASUN L L C,et al.Local Receptive Fields Based Extreme Learning Machine[J].IEEE Computational Intelligence Magazine,2015,10(2):18-29. [13] VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked Denoising Autoencoders:Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408. [14] QIANG Y,JI G,HAN X,et al.Coarse-to-Fine Lung Segmentation in Computed Tomography Images[J].Journal of Computational and Theoretical Nanoscience,2015,12(2):330-334. [15] ZHAO J,MA R,QIANG Y,et al.Solitary Pulmonary Nodule Segmentation Based on the Rolling Ball Method[J].Journal of Computational and Theoretical Nanoscience,2015,12(8):1977-1983. [16] QIANG Y,ZHANG X,JI G,et al.Automated Lung NoduleSegmentation Using an Active Contour Model Based on PET/CT Images[J].Journal of Computational and Theoretical Nanoscience,2015,12(8):1972-1976. [17] TANG J,DENG C,HUANG G B.Extreme Learning Machine for Multilayer Perceptron.[J].IEEE Transactions on Neural Networks & Learning Systems,2015,27(4):809-821. [18] ZHU W,MIAO J,QING L.Constrained Extreme Learning Machines:A Study on Classification Cases[J/OL].Computer Science,https:/arxiv.org/ftp/arxiv/papers/1501/1501/06115.pdf. |
No related articles found! |
|