Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

[21] Xie P(2016)Improving computer-aided detection using convolutional neural networks and random view aggregation IEEE Trans Med Imag 35 1170-44

[22] Zhang L(2016)Automated polyp detection in colonoscopy videos using shape and context information IEEE Trans Med Imag 35 630-30

[23] Schmidt-Hansen M(2016)Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images IEEE Trans Med Imag 35 119-604

[24] Baldwin DR(2014)Weakly supervised histopathology cancer image segmentation and classification Med Image Anal 18 591-13

[25] Hasler E(2016)Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans Scientific Reports 6 1-59

[26] Zamora J(2009)The IASLC Lung Cancer Staging Project: proposals regarding the relevance of TNM in the pathologic staging of small cell lung cancer in the forthcoming (seventh) edition of the TNM classification for lung cancer J Thorac Oncol 4 1049-44

[27] Abraira V(2015)Deep learning Nature 521 436-51

[28] Roque IFM(2012)ImageNet classification with deep convolutional neural networks Adv Neural Inf Proces Syst 25 2012-81

[29] De Leyn P(1999)On the momentum term in gradient descent learning algorithms Neural Netw 12 145-312

[30] Vansteenkiste J(2014)Do we need hundreds of classifiers to solve real world classification problems J Mach Learn Res 15 3133-undefined

[21] Xie P(2016)Improving computer-aided detection using convolutional neural networks and random view aggregation IEEE Trans Med Imag 35 1170-44

[22] Zhang L(2016)Automated polyp detection in colonoscopy videos using shape and context information IEEE Trans Med Imag 35 630-30

[23] Schmidt-Hansen M(2016)Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images IEEE Trans Med Imag 35 119-604

[24] Baldwin DR(2014)Weakly supervised histopathology cancer image segmentation and classification Med Image Anal 18 591-13

[25] Hasler E(2016)Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans Scientific Reports 6 1-59

[26] Zamora J(2009)The IASLC Lung Cancer Staging Project: proposals regarding the relevance of TNM in the pathologic staging of small cell lung cancer in the forthcoming (seventh) edition of the TNM classification for lung cancer J Thorac Oncol 4 1049-44

[27] Abraira V(2015)Deep learning Nature 521 436-51

[28] Roque IFM(2012)ImageNet classification with deep convolutional neural networks Adv Neural Inf Proces Syst 25 2012-81

[29] De Leyn P(1999)On the momentum term in gradient descent learning algorithms Neural Netw 12 145-312

[30] Vansteenkiste J(2014)Do we need hundreds of classifiers to solve real world classification problems J Mach Learn Res 15 3133-undefined