暂无分享,去创建一个
Tom Vercauteren | Paul Suetens | Dirk Vandermeulen | Frederik Maes | Bart Jansen | Abel D'iaz Berenguer | Hichem Sahli | Boris Joukovsky | Maryna Kvasnytsia | Ine Dirks | Mitchel Alioscha-Perez | Nikolaos Deligiannis | Panagiotis Gonidakis | Sebasti'an Amador S'anchez | Redona Brahimetaj | Evgenia Papavasileiou | Jonathan Cheung-Wai Chana | Fei Li | Shangzhen Song | Yixin Yang | Sofie Tilborghs | Siri Willems | Tom Eelbode | Jeroen Bertels | Lucas Fidon | David Robben | Arne Brys | Dirk Smeets | Bart Ilsen | Nico Buls | Nina Watt'e | Johan de Mey | Annemiek Snoeckx | Paul M. Parizel | Julien Guiot | Louis Deprez | Paul Meunier | Stefaan Gryspeerdt | Kristof De Smet | Jef Vandemeulebroucke | T. Vercauteren | D. Smeets | J. Vandemeulebroucke | P. Parizel | H. Sahli | F. Maes | D. Vandermeulen | P. Suetens | B. Jansen | Yixin Yang | Shangzhen Song | P. Meunier | B. Ilsen | J. Mey | J. Guiot | L. Deprez | D. Robben | N. Buls | Mitchel Alioscha-Pérez | S. Gryspeerdt | Evgenia Papavasileiou | Tom Eelbode | K. Smet | A. Snoeckx | Abel D'iaz Berenguer | B. Joukovsky | Maryna Kvasnytsia | Ine Dirks | Nikolaos Deligiannis | Panagiotis Gonidakis | Redona Brahimetaj | Fei Li | S. Tilborghs | S. Willems | J. Bertels | Lucas Fidon | Arne Brys | Nina Watt'e
[1] Marek J. Druzdzel,et al. Learning Bayesian network parameters from small data sets: application of Noisy-OR gates , 2001, Int. J. Approx. Reason..
[2] Arthur C. Sanderson,et al. JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.
[3] Richard C. Pais,et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.
[4] Eugene Demidenko,et al. Confidence intervals and bands for the binormal ROC curve revisited , 2012, Journal of applied statistics.
[5] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[7] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[8] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.
[9] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[10] Jian Sun,et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[12] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[13] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[14] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[15] Anna Shcherbina,et al. Not Just a Black Box: Learning Important Features Through Propagating Activation Differences , 2016, ArXiv.
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[19] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[20] Sébastien Ourselin,et al. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks , 2017, BrainLes@MICCAI.
[21] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[23] Tao Mei,et al. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[26] T. Pajdla,et al. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition , 2015, Computer Vision and Pattern Recognition.
[27] Sebastian Kmiec,et al. Learnable Pooling Methods for Video Classification , 2018, ECCV Workshops.
[28] David Pfau,et al. Towards a Definition of Disentangled Representations , 2018, ArXiv.
[29] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[30] Nima Tajbakhsh,et al. UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.
[31] Peter Filzmoser,et al. nsROC: An R package for Non-Standard ROC Curve Analysis , 2018, R J..
[32] Zhe Li,et al. Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[33] Dmitry Vetrov,et al. Variational Autoencoder with Arbitrary Conditioning , 2018, ICLR.
[34] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] Klaus-Robert Müller,et al. Layer-Wise Relevance Propagation: An Overview , 2019, Explainable AI.
[36] Xavier Binefa,et al. Learning Disentangled Representations with Reference-Based Variational Autoencoders , 2019, ICLR 2019.
[37] Yuan Gao,et al. Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images , 2020, IEEE Access.
[38] Hayit Greenspan,et al. Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis , 2020, ArXiv.
[39] Wenyu Liu,et al. A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT , 2020, IEEE Transactions on Medical Imaging.
[40] Ali Gholamrezanezhad,et al. Coronavirus disease 2019 (COVID-19) imaging reporting and data system (COVID-RADS) and common lexicon: a proposal based on the imaging data of 37 studies , 2020, European Radiology.
[41] Bram van Ginneken,et al. CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation , 2020, Radiology.
[42] Kaijin Xu,et al. A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia , 2020, Engineering.
[43] K. Cao,et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.
[44] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[45] Haibo Xu,et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system , 2020, Applied Soft Computing.
[46] A. Amyar,et al. Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation , 2020, medRxiv.
[47] Yuedong Yang,et al. Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[48] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] P. Xie,et al. COVID-CT-Dataset: A CT Scan Dataset about COVID-19 , 2020, ArXiv.
[50] H. Kauczor,et al. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society , 2020, Radiology.
[51] Z. Fayad,et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection , 2020, Radiology.
[52] X. He,et al. Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans , 2020, medRxiv.
[53] Z. Fayad,et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) , 2020, Radiology.
[54] Weiliang Tang,et al. Chest CT for detecting COVID-19: a systematic review and meta-analysis of diagnostic accuracy , 2020, European Radiology.
[55] Sebasti'an Amador S'anchez,et al. Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients , 2020, ArXiv.
[56] Rabha W. Ibrahim,et al. Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features , 2020, Entropy.
[57] Jonathan H. Chung,et al. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA , 2020, Journal of thoracic imaging.
[58] D. Shen,et al. Lung Infection Quantification of COVID-19 in CT Images with Deep Learning , 2020, ArXiv.
[59] K. Cao,et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .
[60] Kai Zhao,et al. Res2Net: A New Multi-Scale Backbone Architecture , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Yaozong Gao,et al. Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification , 2021, Physics in medicine and biology.
[62] Ming-Ming Cheng,et al. JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation , 2020, IEEE Transactions on Image Processing.
[63] Shuangjia Zheng,et al. Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics.