References
- Barbu A, She Y, Ding L, Gramajo G. 2013. Feature selection with annealing for big data learning. arXiv preprint arXiv:1310.2880.
- Chapelle O. 2007. Training a support vector machine in the primal. Neural Comput. 19:1155–1178.
- Cherry KM, Wang S, Turkbey EB, Summers RM. 2014. Abdominal lymphadenopathy detection using random forest. In: SPIE Medical Imaging, San Diego, CA.
- Crammer K, Singer Y. 2002. On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res. 2:265–292.
- Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. 2014. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, Orlando, FL; p. 675–678.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In: NIPS, Lake Tahoe, NV; p. 1097–1105.
- LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proc IEEE. 86:2278–2324.
- Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z. 2014. Deeply-supervised nets. AISTATS 2015, San Diego, CA.
- Lee Y, Lin Y, Wahba G. 2004. Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data. J Am Stat Assoc. 99:67–81.
- Liu J, Zhao J, Hoffman J, Yao J, Zhang W, Turkbey EB, Wang S, Kim C, Summers RM. 2014. Mediastinal lymph node detection on thoracic CT scans using spatial prior from multi-atlas label fusion. In: SPIE Medical Imaging, San Diego, CA.
- Masnadi-Shirazi H, Mahadevan V, Vasconcelos N. 2010. On the design of robust classifiers for computer vision. In: CVPR, San Francisco, CA; p. 779–786.
- Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. 2013. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: MICCAI, Nagoya, Japan; p. 246–253.
- Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM. 2014. A new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations. In: MICCAI, Boston, MA; p. 520–527.
- Therasse P, Arbuck S, Eisenhauer E, Wanders J, Kaplan R, Rubinstein L, et al. 2000. New guidelines to evaluate the response to treatment in solid tumors. JNCI. 92:205–216.
- Vapnik VN, Vapnik V. 1998. Statistical learning theory, Vol. 1. New York, NY: Wiley.
- Wan L, Zeiler M, Zhang S, Cun YL, Fergus R. 2013. Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), Atlanta, GA; p. 1058–1066.