暂无分享,去创建一个
Gustavo Carneiro | Yuyuan Liu | Gabriel Maicas | Rajvinder Singh | Johan W. Verjans | Yu Tian | Leonardo Zorron Cheng Tao Pu | Alastair D. Burt | Seon Ho Shin | Seon Ho Shin | G. Carneiro | Rajvinder Singh | A. Burt | J. Verjans | Gabriel Maicas | L. Pu | Yuyuan Liu | Yu Tian
[1] Jihoon Kim,et al. Calibrating predictive model estimates to support personalized medicine , 2011, J. Am. Medical Informatics Assoc..
[2] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Deng Cai,et al. Laplacian Score for Feature Selection , 2005, NIPS.
[4] Doina Precup,et al. Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation , 2018, MICCAI.
[5] Konstantinos Kamnitsas,et al. Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation , 2019, MICCAI.
[6] Ling Chen,et al. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection , 2018, KDD.
[7] David Lieberman,et al. Colorectal Cancer Screening: Recommendations for Physicians and Patients from the U.S. Multi-Society Task Force on Colorectal Cancer , 2017, The American Journal of Gastroenterology.
[8] Ruslan Salakhutdinov,et al. A Simple Approach to the Noisy Label Problem Through the Gambler's Loss , 2019 .
[9] A. Jemal,et al. Colorectal cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.
[10] M. Kudo,et al. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience , 2017, Oncology.
[11] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[12] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[13] Carl Doersch,et al. Tutorial on Variational Autoencoders , 2016, ArXiv.
[14] P. Bossuyt,et al. Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review , 2006, The American Journal of Gastroenterology.
[15] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[17] Ramesh Nallapati,et al. OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Noriya Uedo,et al. Narrow‐band imaging with dual focus magnification in differentiating colorectal neoplasia , 2013, Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society.
[19] Gustavo Carneiro,et al. Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy , 2020, MICCAI.
[20] Longbing Cao,et al. Deep Learning for Anomaly Detection: A Review , 2020, ArXiv.
[21] Michael I. Jordan,et al. Bayesian parameter estimation via variational methods , 2000, Stat. Comput..
[22] Gustavo Carneiro,et al. Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy , 2020, Medical Image Anal..
[23] Alex Kendall,et al. Concrete Dropout , 2017, NIPS.
[24] S. E. Ahmed,et al. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference , 2008, Technometrics.
[25] Hayit Greenspan,et al. Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[26] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[27] Saeed Hassanpour,et al. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images , 2017, Journal of pathology informatics.
[28] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[30] Joseph Bullock,et al. XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets , 2018, Medical Imaging.
[31] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[32] Ahmedin Jemal,et al. Global patterns and trends in colorectal cancer incidence and mortality , 2016, Gut.
[33] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[34] Aymeric Histace,et al. Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge , 2017, IEEE Transactions on Medical Imaging.
[35] Shoichi Saito,et al. Validation study for development of the Japan NBI Expert Team classification of colorectal lesions , 2018, Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society.
[36] Jürgen Schmidhuber,et al. Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.
[37] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[39] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[40] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[41] Yongxin Yang,et al. Learning to Generalize: Meta-Learning for Domain Generalization , 2017, AAAI.
[42] Anton van den Hengel,et al. Deep Anomaly Detection with Deviation Networks , 2019, KDD.
[43] 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.
[44] David Cohn,et al. Active Learning , 2010, Encyclopedia of Machine Learning.
[45] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[46] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[47] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[48] Gustavo Carneiro,et al. Computer-aided diagnosis for characterisation of colorectal lesions: a comprehensive software including serrated lesions. , 2020, Gastrointestinal endoscopy.
[49] Gustavo Carneiro,et al. Sa1908 COMPUTER-AIDED DIAGNOSIS FOR CHARACTERISING COLORECTAL LESIONS: INTERIM RESULTS OF A NEWLY DEVELOPED SOFTWARE , 2018, Gastrointestinal Endoscopy.
[50] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[51] M. Jorge Cardoso,et al. Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions , 2018, MICCAI.
[52] Shinji Tanaka,et al. Validation of a simple classification system for endoscopic diagnosis of small colorectal polyps using narrow-band imaging. , 2012, Gastroenterology.
[53] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[54] Shinji Tanaka,et al. Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[55] Svetha Venkatesh,et al. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[56] B. Ripley,et al. Pattern Recognition , 1968, Nature.
[57] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[58] B. Levin,et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. , 2008, Gastroenterology.
[59] Yuyuan Liu,et al. Photoshopping Colonoscopy Video Frames , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
[60] Georg Langs,et al. f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks , 2019, Medical Image Anal..
[61] Shinji Tanaka,et al. Endoscopic prediction of deep submucosal invasive carcinoma: validation of the narrow-band imaging international colorectal endoscopic (NICE) classification. , 2013, Gastrointestinal endoscopy.