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[1] Xiaohong W. Gao,et al. Classification of CT brain images based on deep learning networks , 2017, Comput. Methods Programs Biomed..
[2] Dongdong Chen,et al. Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations , 2020, MICCAI.
[3] Dinggang Shen,et al. BIRNet: Brain image registration using dual‐supervised fully convolutional networks , 2018, Medical Image Anal..
[4] Jiancheng Lv,et al. Unsupervised Multi-Manifold Clustering by Learning Deep Representation , 2017, AAAI Workshops.
[5] Vivek Kumar Singh,et al. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network , 2018, Expert Syst. Appl..
[6] Heung-Il Suk,et al. Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection , 2018, Neural Networks.
[7] Gustavo Carneiro,et al. Deep structured learning for mass segmentation from mammograms , 2014, 2015 IEEE International Conference on Image Processing (ICIP).
[8] Dongdong Chen,et al. An Improved Dual-Channel Network to Eliminate Catastrophic Forgetting , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[9] Dongdong Chen,et al. Geometry of Deep Learning for Magnetic Resonance Fingerprinting , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[10] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Yen-Wei Chen,et al. Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic Ct Images , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[12] B. Stewart,et al. World Cancer Report , 2003 .
[13] Gustavo Carneiro,et al. A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..
[14] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[16] Miguel Ángel Guevara-López,et al. Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..
[17] N Karssemeijer,et al. Use of border information in the classification of mammographic masses , 2006, Physics in medicine and biology.
[18] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Ezzeddine Zagrouba,et al. Breast cancer diagnosis in digitized mammograms using curvelet moments , 2015, Comput. Biol. Medicine.
[20] Richard H. Moore,et al. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .
[21] Xiaohui Xie,et al. Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification , 2016, bioRxiv.
[22] Zhang Yi,et al. Graph Regularized Restricted Boltzmann Machine , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[23] Mike E. Davies,et al. Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net , 2018, RAMBO+BIA+TIA@MICCAI.
[24] Dongdong Chen,et al. Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis , 2019, MICCAI.
[25] P. Boyle,et al. World Cancer Report 2008 , 2009 .
[26] Rainer Stotzka,et al. An Example-Based System to Support the Segmentation of Stellate Lesions , 2005, Bildverarbeitung für die Medizin.
[27] Gustavo Carneiro,et al. The Automated Learning of Deep Features for Breast Mass Classification from Mammograms , 2016, MICCAI.
[28] Jian Zhang,et al. Deep Generative Breast Cancer Screening and Diagnosis , 2018, MICCAI.
[29] Tae-Seong Kim,et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification , 2018, Int. J. Medical Informatics.
[30] Jaime S. Cardoso,et al. INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.
[31] Daniel L Rubin,et al. A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.
[32] Andrew P. Bradley,et al. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.
[33] Stanislaw Osowski,et al. Novel methods of image description and ensemble of classifiers in application to mammogram analysis , 2017, Expert Syst. Appl..
[34] Zhedong Zheng,et al. Dual-path Convolutional Image-Text Embeddings with Instance Loss , 2017, ACM Trans. Multim. Comput. Commun. Appl..
[35] Stefan Neubauer,et al. Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach , 2020, Medical Image Anal..
[36] R. B. Potts. Some generalized order-disorder transformations , 1952, Mathematical Proceedings of the Cambridge Philosophical Society.
[37] Li Shen,et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography , 2017, Scientific Reports.
[38] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[39] Tae-Seong Kim,et al. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram. , 2020, Advances in experimental medicine and biology.
[40] Dongdong Chen,et al. A Deep Dual-path Network for Improved Mammogram Image Processing , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[41] Mike E. Davies,et al. Deep Decomposition Learning for Inverse Imaging Problems , 2020, ECCV.
[42] N. Karssemeijer,et al. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network , 2017, Medical physics.
[43] A. Ramli,et al. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. , 2013, Clinical imaging.
[44] Bram van Ginneken,et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning , 2016, Scientific Reports.
[45] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[46] Arnau Oliver,et al. A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..
[47] Jaime S. Cardoso,et al. Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer , 2015, Int. J. Pattern Recognit. Artif. Intell..
[48] Yide Ma,et al. Multi-level nested pyramid network for mass segmentation in mammograms , 2019, Neurocomputing.
[49] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[50] Xiaogang Wang,et al. CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[51] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[52] Aimin Hao,et al. Multi-view multi-scale CNNs for lung nodule type classification from CT images , 2018, Pattern Recognit..
[53] Gregory D. Hager,et al. Adversarial deep structured nets for mass segmentation from mammograms , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[54] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[55] Gustavo Carneiro,et al. Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms , 2015, MICCAI.