AI for radiographic COVID-19 detection selects shortcuts over signal
暂无分享,去创建一个
Joseph D. Janizek | Alex J. DeGrave | A. J. DeGrave | J. D. Janizek | S.-I. Lee | Su-In Lee | J. Janizek | A. DeGrave | Joseph D. Janizek
[1] Loris Nanni,et al. A critic evaluation of methods for COVID-19 automatic detection from X-ray images , 2020, Information Fusion.
[2] Percy Liang,et al. An Investigation of Why Overparameterization Exacerbates Spurious Correlations , 2020, ICML.
[3] U. Rajendra Acharya,et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.
[4] Pascal Sturmfels,et al. Learning Explainable Models Using Attribution Priors , 2019, ArXiv.
[5] Gustavo Carneiro,et al. Detecting hip fractures with radiologist-level performance using deep neural networks , 2017, ArXiv.
[6] Ciarán M Lee,et al. Improving the accuracy of medical diagnosis with causal machine learning , 2020, Nature Communications.
[7] Jonathan R. Medverd,et al. Policies and Guidelines for COVID-19 Preparedness: Experiences from the University of Washington , 2020, Radiology.
[8] Gabriel Erion,et al. An adversarial approach for the robust classification of pneumonia from chest radiographs , 2020, CHIL.
[9] Joseph Paul Cohen,et al. COVID-19 Image Data Collection , 2020, ArXiv.
[10] P. Sham,et al. A note on the calculation of empirical P values from Monte Carlo procedures. , 2002, American journal of human genetics.
[11] Andrea Laghi,et al. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence , 2020, The Lancet Digital Health.
[12] Joseph Paul Cohen,et al. COVID-19 Image Data Collection: Prospective Predictions Are the Future , 2020, The Journal of Machine Learning for Biomedical Imaging.
[13] Antonio Pertusa,et al. PadChest: A large chest x-ray image dataset with multi-label annotated reports , 2019, Medical Image Anal..
[14] Dong-Hyun Kim,et al. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification , 2020, Comput. Methods Programs Biomed..
[15] Andrew Y. Ng,et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.
[16] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Daniel C. Castro,et al. Causality matters in medical imaging , 2019, Nature Communications.
[19] N. Arun,et al. Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging , 2020, medRxiv.
[20] Ciarán M Lee,et al. Publisher Correction: Improving the accuracy of medical diagnosis with causal machine learning , 2020, Nature communications.
[21] Alexander Wong,et al. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.
[22] Pascal Sturmfels,et al. Visualizing the Impact of Feature Attribution Baselines , 2020 .
[23] Marcus A. Badgeley,et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study , 2018, PLoS medicine.
[24] Brian Pollack,et al. Explanation by Progressive Exaggeration , 2020, ICLR.
[25] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[26] B. Beck,et al. Cross-sectional study , 2011 .
[27] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[28] Sameer Singh,et al. Beyond Accuracy: Behavioral Testing of NLP Models with CheckList , 2020, ACL.
[29] Allan Tucker,et al. Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection , 2020, ArXiv.
[30] P. Lakhani,et al. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.
[31] Matthias Bethge,et al. Shortcut Learning in Deep Neural Networks , 2020, Nat. Mach. Intell..
[32] Charles E Kahn,et al. How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries? , 2020, Radiology. Artificial intelligence.
[33] M. Lenzen,et al. Scientists’ warning on affluence , 2020, Nature Communications.
[34] Antonella Santone,et al. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays , 2020, Computer Methods and Programs in Biomedicine.
[35] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Subhashini Venugopalan,et al. Detection of anaemia from retinal fundus images via deep learning , 2019, Nature Biomedical Engineering.
[37] Mona G. Flores,et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets , 2020, Nature Communications.
[38] Jayashree Kalpathy-Cramer,et al. Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks , 2020, Radiology. Artificial intelligence.
[39] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[40] Pedagógia,et al. Cross Sectional Study , 2019 .
[41] K. Yuen,et al. Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review , 2020, Radiology. Cardiothoracic imaging.
[42] 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.
[43] Stefan Niehues,et al. Comparing different deep learning architectures for classification of chest radiographs , 2020, Scientific Reports.
[44] M. Kuo,et al. Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients , 2019, Radiology.
[45] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[46] Gregory S. Corrado,et al. Detecting Anemia from Retinal Fundus Images , 2019, ArXiv.
[47] A. Ng,et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet , 2018, PLoS medicine.