https://orcid.org/0000-0001-6668-2620
References
- Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA. 2016. Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine. 127:248–257.
- Chatfield K, Simonyan K, Vedaldi A, Zisserman A 2014. Best scientific paper award return of the devil in the details: delving deep into convolutional nets. British Machine Vision Conference. Accessed arxiv.org. https://arxiv.org/pdf/1405.3531v4.pdf.
- Ganesan K, Acharya U, Chua C, Min L, Abraham K, Ng K. 2013. Computer-aided breast cancer detection using mammograms: a review. IEEE Reviews in Biomedical Engineering. 6:77–97.
- Jalalian A, Mashohor S, Mahmud H, Saripan M, Ramli A, Karasfi B. 2013. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging. 37:420–426.
- Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T 2014. Caffe: convolutional architecture for fast feature embedding. Cornell University Library. Report No. : arXiv:1408.5093, Accessed:https://arxiv.org/abs/1408.5093 .
- Jiao Z, Gao X, Wang Y, Li J. 2016. A deep feature based framework for breast masses classification. Neurocomputing. 197:221–231.
- Perre A, 2018. The influence of image normalization in mammographic classification with CNNs. APPIS 2018 – 1st International Conference on Applications of Intelligent Systems; Las Palmas de Gran Canaria, Spain.
- Pisco JM. 2003. Imagiologia basica texto e atlas. 1st ed. lisboa: LIDEL.
- Sampat M, Markey M, Bovik A. 2005. Computer-aided detection and diagnosis in mammography. Handbook of image and video processing. Cambridge, MA: Academic Press Books - Elsevier
- Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging. 35(5):1285–1298.
- Sun W, Tseng TLB, Zhang J, Qian W. 2016. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Computerized medical imaging and graphics 57: 4–9.
- Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J. 2016. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Transactions on Medical Imaging. 35(5):1299–1312.
- Tang JS, Agaian S, Thompason I. 2014. Guest editorial: computer-aided detection or diagnosis (CAD) systems. IEEE Systems Journal. 8:907–909.
- Vedaldi A, Lenc K. 2015. Matconvnet – convolutional neural networks for Matlab. Proceeding of the ACM Int Conf on Multimedia.Cornell University Library. Report No.: arXiv:1412.4564, https://arxiv.org/abs/1412.4564
- Wang D, Khosla A, Gargeya R, Irshad H, Beck A 2016. Deep learning for identifying metastatic breast cancer. Cornell University Library. Report No.: arXiv: submit/1591239. Accessed: https://arxiv.org/pdf/1606.05718.pdf.
- Wichakam I, Vateekul P 2016. Combining deep convolutional networks and svms for mass detection on digital mammograms. 2016 8th International Conference on Knowledge and Smart Technology (KST): 239–244.
- Yi D, Sawyer RL, Cohn III D, Dunnmon J, Lam C, Xiao X, Rubin D 2017. Optimizing and visualizing deep learning for benign/malignant classification in breast tumors. Report No.: arXiv:1705:06362v1. Accessed: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=1705.06362.