Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

[1]  Siwei Lyu,et al.  Average Top-k Aggregate Loss for Supervised Learning , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Colin B. Compas,et al.  Federated learning for predicting clinical outcomes in patients with COVID-19 , 2021, Nature Medicine.

[3]  ShanmugaSundari N,et al.  Automatic COVID-19 Lung Infection Segmentation from CT Images , 2021, SSRN Electronic Journal.

[4]  Colin B. Compas,et al.  Federated Learning used for predicting outcomes in SARS-COV-2 patients , 2021, Research square.

[5]  Qijun Wu,et al.  Imaging characteristics of coronavirus disease 2019 (COVID-19) in pediatric cases: a systematic review and meta-analysis , 2021, Translational pediatrics.

[6]  Su Ruan,et al.  Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism , 2020, Int. J. Imaging Syst. Technol..

[7]  Bradford J. Wood,et al.  Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan , 2020, Medical Image Analysis.

[8]  Angelica I. Avilés-Rivero,et al.  Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans , 2020, Nature Machine Intelligence.

[9]  L. Maier-Hein,et al.  Methods and open-source toolkit for analyzing and visualizing challenge results , 2019, Scientific reports.

[10]  Xutao Li,et al.  A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite , 2021, Computer Science & Information Technology (CS & IT).

[11]  Jens Petersen,et al.  nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.

[12]  William C. Bennett,et al.  Chest imaging representing a COVID-19 positive rural U.S. population , 2020, Scientific Data.

[13]  D. Shen,et al.  Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction , 2020, Medical physics.

[14]  Adel OULEFKI,et al.  Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images , 2020, Pattern Recognition.

[15]  M. Peroni,et al.  Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia , 2020, European Radiology.

[16]  M. Linguraru,et al.  Pediatric lung imaging features of COVID‐19: A systematic review and meta‐analysis , 2020, Pediatric pulmonology.

[17]  L. Larosa,et al.  Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review , 2020, European Journal of Radiology.

[18]  Mona G. Flores,et al.  Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets , 2020, Nature Communications.

[19]  Zhiyong Xu,et al.  A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images , 2020, IEEE Transactions on Medical Imaging.

[20]  Sanjiv Sharma,et al.  CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients , 2020, European Radiology.

[21]  Z. Fayad,et al.  Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 , 2020, Nature Medicine.

[22]  Hongbing Lu,et al.  Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images , 2020, IEEE Transactions on Medical Imaging.

[23]  P. Merkus,et al.  The value of chest CT as a COVID-19 screening tool in children , 2020, European Respiratory Journal.

[24]  Ran Yang,et al.  CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19 , 2020, Korean journal of radiology.

[25]  Dijia Wu,et al.  Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning , 2020, IEEE Transactions on Medical Imaging.

[26]  B. Song,et al.  CT Manifestations and Clinical Characteristics of 1115 Patients with Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis , 2020, Academic Radiology.

[27]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[28]  Raymond Y Huang,et al.  AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT , 2020, Radiology.

[29]  D.-P. Fan,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[30]  Jianfeng Zhang,et al.  CT imaging features of 4121 patients with COVID‐19: A meta‐analysis , 2020, Journal of medical virology.

[31]  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.

[32]  G. Heinze,et al.  Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal , 2020, BMJ.

[33]  Xiaoling Liu,et al.  Imaging and clinical features of patients with 2019 novel coronavirus SARS‐CoV‐2: A systematic review and meta‐analysis , 2020, Journal of medical virology.

[34]  Q. Tao,et al.  Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach , 2020, Radiology. Cardiothoracic imaging.

[35]  Han Zhang,et al.  Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis , 2020, Journal of the American College of Radiology.

[36]  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 .

[37]  K. Cao,et al.  Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.

[38]  Z. Fayad,et al.  Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection , 2020, Radiology.

[39]  A. Yuille,et al.  C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Xiaowei Ding,et al.  Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..

[41]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Jeremiah Neubert,et al.  Deep learning approaches to biomedical image segmentation , 2020 .

[43]  Daguang Xu,et al.  Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation , 2019, MICCAI.

[44]  Daguang Xu,et al.  V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation , 2019, 2019 International Conference on 3D Vision (3DV).

[45]  Yiming Yang,et al.  DARTS: Differentiable Architecture Search , 2018, ICLR.

[46]  Alejandro F. Frangi,et al.  Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions , 2018, ArXiv.

[47]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[48]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[49]  Sanjana Patrick,et al.  Comparison of gray values of cone-beam computed tomography with hounsfield units of multislice computed tomography: An in vitro study , 2017, Indian journal of dental research : official publication of Indian Society for Dental Research.

[50]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[51]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[52]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[53]  Trevor Coward,et al.  An In-Vitro Study , 2016 .

[54]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[55]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[56]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[57]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[59]  Torsten Hothorn,et al.  Exploratory and Inferential Analysis of Benchmark Experiments , 2008 .

[60]  F. Schmidt Meta-Analysis , 2008 .

[61]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[62]  G. Beebe,et al.  Diagnosis at Follow-up. , 1955 .

[63]  Han Zhang,et al.  Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis , 2020, Journal of the American College of Radiology.