暂无分享,去创建一个
Jefersson Alex dos Santos | Hugo Oliveira | Edemir Ferreira | J. A. D. Santos | H. Oliveira | E. Ferreira
[1] Luc Van Gool,et al. The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.
[2] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[3] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[4] Yan Zhang,et al. Deep domain similarity Adaptation Networks for across domain classification , 2018, Pattern Recognit. Lett..
[5] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[6] David R. Dance,et al. Mammographic Image Analysis Society (MIAS) database v1.21 , 2015 .
[7] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[8] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[9] Rui Caseiro,et al. Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[11] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[12] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.
[13] Jiaying Liu,et al. Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..
[14] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Bo Geng,et al. DAML: Domain Adaptation Metric Learning , 2011, IEEE Transactions on Image Processing.
[16] Ling Shao,et al. Transfer Learning for Visual Categorization: A Survey , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[17] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[18] Ye Xu,et al. Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias , 2013, 2013 IEEE International Conference on Computer Vision.
[19] David J. Kriegman,et al. Image to Image Translation for Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[21] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[23] Sethuraman Panchanathan,et al. A Two-Stage Weighting Framework for Multi-Source Domain Adaptation , 2011, NIPS.
[24] Yang Zou,et al. Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training , 2018, ArXiv.
[25] Rama Chellappa,et al. Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.
[26] Jing Zhang,et al. Transfer Learning for Cross-Dataset Recognition: A Survey , 2017, 1705.04396.
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Philip J. Morrow,et al. Fully automated breast boundary and pectoral muscle segmentation in mammograms , 2017, Artif. Intell. Medicine.
[29] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[30] Philip David,et al. A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Bram van Ginneken,et al. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database , 2006, Medical Image Anal..
[32] Shohreh Kasaei,et al. Automatic segmentation of mandible in panoramic x-ray , 2015, Journal of medical imaging.
[33] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Mei Wang,et al. Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.
[35] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[36] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[38] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[39] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[40] Jan Kautz,et al. Unsupervised Image-to-Image Translation Networks , 2017, NIPS.
[42] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[43] Clement J. McDonald,et al. Preparing a collection of radiology examinations for distribution and retrieval , 2015, J. Am. Medical Informatics Assoc..
[44] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[45] Richard H. Moore,et al. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .
[46] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[47] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[48] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[49] Jung-Woo Ha,et al. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[50] Dong Xu,et al. Collaborative and Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[51] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[52] K. Doi,et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. , 2000, AJR. American journal of roentgenology.
[53] Jaime S. Cardoso,et al. INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.
[54] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[55] Vladlen Koltun,et al. Photographic Image Synthesis with Cascaded Refinement Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[56] Jan Kautz,et al. Multimodal Unsupervised Image-to-Image Translation , 2018, ECCV.
[57] Stefan Jaeger,et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. , 2014, Quantitative imaging in medicine and surgery.
[58] Gabriela Csurka,et al. Domain Adaptation for Visual Applications: A Comprehensive Survey , 2017, ArXiv.
[59] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[61] Qiang Ji,et al. Constrained Deep Transfer Feature Learning and Its Applications , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Trevor Darrell,et al. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation , 2016, ArXiv.
[63] Homero Schiabel,et al. Online Mammographic Images Database for Development and Comparison of CAD Schemes , 2011, Journal of Digital Imaging.
[64] Antonio Pertusa,et al. PadChest: A large chest x-ray image dataset with multi-label annotated reports , 2019, Medical Image Anal..
[65] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[66] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[67] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.