Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Aberle, 2011, Reduced lung-cancer mortality with low-dose computed tomographic screening, N. Engl. J. Med., 365, 395, 10.1056/NEJMoa1102873 Aberle, 2011, The national lung screening trial: overview and study design, Radiology, 258, 243, 10.1148/radiol.10091808 Ahn, 2010, Perifissural nodules seen at CT screening for lung cancer, Radiology, 254, 949, 10.1148/radiol.09090031 American Cancer Society, 2014. Cancer Facts and Figures 2014. American College of Radiology, 2014. Lung CT screening reporting and data system (Lung-RADS). Armato, 2011, The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans, Med. Phys., 38, 915, 10.1118/1.3528204 Bar, 2015, Chest pathology detection using deep learning with non-medical training, 294 Bar, 2015, Deep learning with non-medical training used for chest pathology identification, 94140V Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Ciompi, 2014, Automatic classification of perifissural pulmonary nodules in thoracic CT images Ciompi, 2015, Bag of Frequencies: a descriptor of pulmonary nodules in computed tomography images, IEEE Trans. Med. Imaging, 34, 1, 10.1109/TMI.2014.2371821 Dana, 1999, Reflectance and texture of real world surfaces, ACM Trans. Graph., 18, 1, 10.1145/300776.300778 van Ginneken, 2015, Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans, 286 de Hoop, 2012, Pulmonary perifissural nodules on CT scans: rapid growth is not a predictor of malignancy, Radiology, 265, 611, 10.1148/radiol.12112351 Jacobs, 2014, Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images, Med. Image Anal., 18, 374, 10.1016/j.media.2013.12.001 Klik, 2006, Improved classification of pulmonary nodules by automated detection of benign subpleural lymph nodes, 494 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Kuhnigk, 2006, Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans, IEEE Trans. Med. Imaging, 25, 417, 10.1109/TMI.2006.871547 McWilliams, 2013, Probability of cancer in pulmonary nodules detected on first screening CT, N. Engl. J. Med., 369, 910, 10.1056/NEJMoa1214726 Messay, 2010, A new computationally efficient CAD system for pulmonary nodule detection in CT imagery, Med. Image Anal., 14, 390, 10.1016/j.media.2010.02.004 Niemeijer, 2011, On combining computer-aided detection systems, IEEE Trans. Med. Imaging, 30, 215, 10.1109/TMI.2010.2072789 Pastorino, 2012, Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the mild trial, Eur. J. Cancer Prev., 21, 308, 10.1097/CEJ.0b013e328351e1b6 Pedersen, 2009, The Danish randomized lung cancer CT screening trial – overall design and results of the prevalence round, J. Thorac. Oncol., 4, 608, 10.1097/JTO.0b013e3181a0d98f Razavian, 2014, CNN features off-the-shelf: an astounding baseline for recognition Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L., 2014. ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575. Rutter, 2000, Bootstrap estimation of diagnostic accuracy with patient-clustered data, Acad. Radiol., 7, 413, 10.1016/S1076-6332(00)80381-5 Sermanet, 2014, OverFeat:integrated recognition, localization and detection using convolutional networks Teramoto, 2013, Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter, Int. J. Comput. Assist. Radiol. Surg., 8, 193, 10.1007/s11548-012-0767-5 van den Bergh, 2008, Impact of computed tomography screening for lung cancer on participants in a randomized controlled trial (NELSON trial), Cancer, 113, 396, 10.1002/cncr.23590 Vapnik, 1995