Supervised Contrastive Pre-training forMammographic Triage Screening Models
暂无分享,去创建一个
Peng Chang | Mei Han | Yanbo Zhang | Zhicheng Yang | Jie Ma | Jing Xiao | Yuxing Tang | Zhenjie Cao | Yanbo Zhang | Mei Han | Peng Chang | Jie Ma | Yuxing Tang | Jing Xiao | Zhenjie Cao | Zhicheng Yang
[1] Ce Liu,et al. Supervised Contrastive Learning , 2020, NeurIPS.
[2] David S. Melnick,et al. International evaluation of an AI system for breast cancer screening , 2020, Nature.
[3] Artificial Intelligence to Support Independent Assessment of Screening Mammograms-The Time Has Come. , 2020, JAMA oncology.
[4] Li Shen,et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography , 2017, Scientific Reports.
[5] Samy Bengio,et al. Large Scale Online Learning of Image Similarity through Ranking , 2009, IbPRIA.
[6] R Devon Hjelm,et al. Learning Representations by Maximizing Mutual Information Across Views , 2019, NeurIPS.
[7] Geoffrey E. Hinton,et al. Big Self-Supervised Models are Strong Semi-Supervised Learners , 2020, NeurIPS.
[8] Zhijun Hu,et al. Multi-View Convolutional Neural Networks for Mammographic Image Classification , 2019, IEEE Access.
[9] S. Zackrisson,et al. Identifying normal mammograms in a large screening population using artificial intelligence , 2020, European Radiology.
[10] Daniel L Rubin,et al. A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.
[11] Ender Konukoglu,et al. Contrastive learning of global and local features for medical image segmentation with limited annotations , 2020, NeurIPS.
[12] X. Castells,et al. Risk of Breast Cancer in Women with False-Positive Results according to Mammographic Features. , 2016, Radiology.
[13] Manohar Paluri,et al. Metric Learning with Adaptive Density Discrimination , 2015, ICLR.
[14] Karla Kerlikowske,et al. Factors Associated With Rates of False-Positive and False-Negative Results From Digital Mammography Screening: An Analysis of Registry Data , 2016, Annals of Internal Medicine.
[15] C. Lehman,et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. , 2017, Radiology.
[16] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[17] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[18] Chengxu Zhuang,et al. Local Aggregation for Unsupervised Learning of Visual Embeddings , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[20] E. Conant,et al. Can AI Help Make Screening Mammography "Lean"? , 2019, Radiology.
[21] Basit Raza,et al. Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network , 2019, IEEE Access.
[22] T. Helbich,et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study , 2019, European Radiology.
[23] Lior Ness,et al. Multi-task Learning for Detection and Classification of Cancer in Screening Mammography , 2020, MICCAI.
[24] R. Barzilay,et al. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. , 2019, Radiology.
[25] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[26] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[27] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Nan Wu,et al. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.