[1] Sluimer I, Schilham A, Prokop M et al. Computer analysis of computed tomography scans of the lung: a survey[J]. IEEE Transactions on Medical Imaging, 25, 385-405(2006).
[2] Gao T, Gong J, Wang Y J et al. Three dimensional adaptive template matching algorithm for lung nodule detection[J]. Journal of Image and Graphics, 19, 1384-1391(2014).
[3] Murphy K, Ginneken B V. Schilham A M R, et al. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification[J]. Medical Image Analysis, 13, 757-770(2009).
[4] Jacobs C. Rikxoort E M V, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images[J]. Medical Image Analysis, 18, 374-384(2014).
[5] Chen S, Li L. A new computer aided diagnostic scheme for lung nodule detection on chest radiograph[J]. Acta Electronica Sinica, 38, 1211-1216(2010).
[6] Wang M, Liu K X, Liu L et al. Super-resolution reconstruction of image based on optimized convolution neural network[J]. Laser & Optoelectronics Progress, 54, 111005(2017).
[7] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[8] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[S.l.]. International Conference on Medical Image Computing and Computer-Assisted Intervention, [S.l.]: Springer, 234-241(2015).
[9] Huang H, He K, Zheng X L et al. Spatial-spectral feature extraction of hyperspectral image based on deep learning[J]. Laser & Optoelectronics Progress, 54, 101001(2017).
[10] Chen H, Qi X, Yu L et al. DCAN: deep contour-aware networks for accurate gland segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, 16541200(2016).
[11] Kamnitsas K, Ledig C. Newcombe V F J, et al. Efficient multiscale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]. Medical Image Analysis, 36, 61-78(2017).
[12] Setio A 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, 35, 1160-1169(2016).
[13] van Ginneken B v, Setio A A A, Jacobs C et al. . Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans[C]. IEEE 12th International Symposium on Biomedical Imaging, 15309667(2015).
[14] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]. International Conference on Machine Learning, 448-456(2015).
[15] He K, Zhang X, Ren S et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]. IEEE International Conference on Computer Vision, 1026-1034(2015).
[16] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[C]. 14th International Conference on Artificial Intelligence and Statistics, 315-323(2011).
[17] Oliveira G L, Valada A, Bollen C et al. Deep learning for human part discovery in images[C]. IEEE International Conference on Robotics and Automation, 16055460(2016).
[18] Brown M S, Lo P, Goldin J G et al. Toward clinically usable CAD for lung cancer screening with computed tomography[J]. European Radiology, 24, 2719-2728(2014).
[19] Torres E L, Fiorina E, Pennazio F. et al. Large scale validation of the M5L lung CAD on heterogeneous CT datasets[J]. Medical Physics, 42, 1477-1489(2015).
[20] Choi W J, Choi T S. Automated pulmonary nodule detection system in computed tomography images: a hierarchical block classification approach[J]. Entropy, 15, 507-523(2013).
[21] Lii S G A, Mclennan G, Bidaut L et al. . The lung image database consortium(LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans[J]. Medical Physics, 38, 915-931(2011).
[22] van Ginneken B, Lii S G A, de Hoop B et al. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study[J]. Medical Image Analysis, 14, 707-722(2010).
[23] Liu F, Liu P Y, Li B et al. Deep learning model design of video target tracking based on TensorFlow platform[J]. Laser & Optoelectronics Progress, 54, 091501(2017).
[24] Egan J P, Greenberg G Z, Schulman A I. Operating characteristics, signal detectability, and the method of free response[J]. Journal of the Acoustical Society of America, 33, 993-1007(1961).
[25] Cascio D, Magro R, Fauci F et al. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models[J]. Computers in Biology & Medicine, 42, 1098-1109(2012).