Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features
暂无分享,去创建一个
Yu-Dong Yao | Zhiqiong Wang | Hao Zhang | Junchang Xin | Huaxia Wang | Mo Li | Hanyu Jiang | Yu-dong Yao | Junchang Xin | Huaxia Wang | Zhiqiong Wang | Hao Zhang | Mo Li | Hanyu Jiang | Yu-dong Yao
[1] Xue-wen Chen,et al. Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.
[2] P. Engstrom,et al. Mammography adherence and psychological distress among women at risk for breast cancer. , 1993, Journal of the National Cancer Institute.
[3] Samuel Cheng,et al. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology , 2016, SPIE Medical Imaging.
[4] Xinbo Gao,et al. A deep feature based framework for breast masses classification , 2016, Neurocomputing.
[5] Reyer Zwiggelaar,et al. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks , 2018, IEEE Journal of Biomedical and Health Informatics.
[6] Dansheng Song,et al. Ipsilateral-mammogram computer-aided detection of breast cancer. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[7] C. K. Chua,et al. Computer-Aided Breast Cancer Detection Using Mammograms: A Review , 2013, IEEE Reviews in Biomedical Engineering.
[8] Dawn Provenzale,et al. Colorectal Cancer Statistics From the Veterans Affairs Central Cancer Registry. , 2016, Clinical colorectal cancer.
[9] P. Huynh,et al. The false-negative mammogram. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.
[10] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[11] Abdulhamit Subasi,et al. Breast cancer diagnosis using GA feature selection and Rotation Forest , 2015, Neural Computing and Applications.
[12] Ge Yu,et al. Breast tumor detection in digital mammography based on extreme learning machine , 2014, Neurocomputing.
[13] Andrew P. Bradley,et al. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.
[14] H. Chenga,et al. Automated breast cancer detection and classification using ultrasound images A survey , 2009 .
[15] K. V. Arya,et al. Low-dose CT image reconstruction using gain intervention-based dictionary learning , 2018 .
[16] Lubomir M. Hadjiiski,et al. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. , 2016, Medical physics.
[17] Fei Li,et al. Detection of Suspicious Lesions by Adaptive Thresholding Based on Multiresolution Analysis in Mammograms , 2011, IEEE Transactions on Instrumentation and Measurement.
[18] Cheng Wu,et al. Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.
[19] Marcelo Zanchetta do Nascimento,et al. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm , 2014, Comput. Methods Programs Biomed..
[20] Heye Zhang,et al. Robust estimation of carotid artery wall motion using the elasticity‐based state‐space approach , 2017, Medical Image Anal..
[21] Wenqing Sun,et al. A Preliminary Study on Breast Cancer Risk Analysis Using Deep Neural Network , 2016, Digital Mammography / IWDM.
[22] Haidi Ibrahim,et al. Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement , 2007, IEEE Transactions on Consumer Electronics.
[23] Yogesh Kumar,et al. An efficient and robust approach for biomedical image retrieval using Zernike moments , 2018, Biomed. Signal Process. Control..
[24] Jian Wang,et al. Breast density classification using histogram moments of multiple resolution mammograms , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.
[25] P Haddawy,et al. Construction of a Bayesian network for mammographic diagnosis of breast cancer , 1997, Comput. Biol. Medicine.
[26] Shailendra Tiwari. A variational framework for low-dose sinogram restoration , 2017 .
[27] Miguel Ángel Guevara-López,et al. Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..
[28] J. Harford,et al. Breast-cancer early detection in low-income and middle-income countries: do what you can versus one size fits all. , 2011, The Lancet. Oncology.
[29] Hong Liu,et al. Marker-Controlled Watershed for Lesion Segmentation in Mammograms , 2011, Journal of Digital Imaging.
[30] Umi Kalthum Ngah,et al. Density Based Breast Segmentation for Mammograms Using Graph Cut and Seed Based Region Growing Techniques , 2010, 2010 Second International Conference on Computer Research and Development.
[31] C. Floyd,et al. Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. , 2006, Medical physics.
[32] Dong Liang,et al. Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach , 2018, IEEE Transactions on Medical Imaging.
[33] Nikos Dimitropoulos,et al. A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry , 2011, Comput. Methods Programs Biomed..
[34] Qiguang Miao,et al. A Semi-Supervised Image Classification Model Based on Improved Ensemble Projection Algorithm , 2018, IEEE Access.
[35] Ghassan Hamarneh,et al. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification , 2017, Comput. Methods Programs Biomed..
[36] Yongyi Yang,et al. Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.
[37] Daniel L. Rubin,et al. Probabilistic visual search for masses within mammography images using deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[38] Yingle Fan,et al. Dual Learning-Based Safe Semi-Supervised Learning , 2018, IEEE Access.