4. Doi K. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 2007, 31(4/5): 198-211.
[5]
5. Xu Yan, Jia Zhipeng, Ai Yuqing, et al. Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brisbane: IEEE, 2015: 947-951.
[6]
6. Cheng J Z, Ni D, Chou Y H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
13. LISA lab, University of Montreal. Deep learning tutorial release 0.1[EB/OL]. (2015-09-01)[2016-09-20]. http://deeplearning.net/ tutorial/contents.html.
15. Dhungel N, Carneiro G, Bradley A P. Deep structured learning for mass segmentation from mammograms//IEEE International Conference on Image Processing (ICIP). Québec: IEEE, 2015: 2950-2954.
[16]
16. Dhungel N, Carneiro G, Bradley A P. Deep learning and structured prediction for the segmentation of mass in mammograms//Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9349(0302-9743): 605-612.
[17]
17. Cha K H, Hadjiiski L M, Samala R K, et al. Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 978512-978517.
[18]
18. Su Hai, Liu Fujun, Xie Yuanpu, et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). New York: IEEE, 2015: 55-58.
[19]
19. Nowozin S, Lampert C H. Structured learning and prediction in computer vision. Foundations and Trends？ in Computer Graphics and Vision, 2011, 6(3/4): 185-365.
[20]
20. Cho J, Lee K, Shin E, et al. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy[EB/OL]. (2016-01-07)[2016-09-20]. https://arxiv. org/abs/1511.06348.
[21]
21. Carneiro G, Nascimento J, Bradley A P. Unregistered multiview mammogram analysis with pre-trained deep learning models//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9351(0302-9743): 652-660.
[22]
22. 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. Onco Targets Ther, 2015, 8: 2015-2022.
[23]
23. Shen W, Zhou M, Yang F, et al. Multi-scale Convolutional Neural Networks for lung nodule classification//Information Processing in Medical Imaging. Isle of Skye: Springer, 2015, 9123(0302-9743): 588-599.
[24]
24. Xu Jun, Xiang Lei, Hang Renlong, et al. Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology//2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE, 2014: 999-1002.
[25]
25. Xu Jun, Xiang Lei, Liu Qingshan, et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging, 2016, 35(1): 119-130.
[26]
26. Arevalo J, González F A, Ramos-Pollán R, et al. Convolutional neural networks for mammography mass lesion classi-fication//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano: IEEE, 2015: 797-800.
[27]
27. Arevalo J, González F A, Ramos-Pollán R, et al. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed, 2016, 127: 248-257.
[28]
28. Fotin S V, Yin Y, Haldankar H, et al. Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 97850X-97856.
[29]
29. Wu Kaizhi, Chen Xi, Ding Mingyue. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik (Stuttg), 2014, 125(15): 4057-4063.
[30]
30. Liu Xiao, Shi Jun, Zhang Qi. Tumor classification by deep polynomial network and multiple kernel learning on small ultrasound image dataset//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 313-320.
[31]
31. Shi Jun, Zhou Shichong, Liu Xiao, et al. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 2016, 194: 87-94.
[32]
32. Abdel-Zaher A M, Eldeib A M. Breast cancer classification using deep belief networks. Expert Syst Appl, 2016, 46: 139-144.
[33]
33. Masood A, Al-Jumaily A, Anam K. Self-supervised learning model for skin cancer diagnosis//Neural Engineering (NER) 2015: 7th International IEEE/EMBS Conference. Montpellier: IEEE, 2015: 1012-1015.
[34]
34. Codella N, Cai J, Abedini M, et al. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 118-126.
