暂无分享,去创建一个
Tom Vercauteren | Paul Suetens | Dirk Vandermeulen | Frederik Maes | Ine Dirks | Panagiotis Gonidakis | Sebasti'an Amador S'anchez | Sofie Tilborghs | Siri Willems | Jeroen Bertels | Lucas Fidon | David Robben | Arne Brys | Dirk Smeets | Bart Ilsen | Johan de Mey | Annemiek Snoeckx | Paul M. Parizel | Jef Vandemeulebroucke | Tom Eelbode | Adriana Dubbeldam | Nico Buls | T. Vercauteren | D. Smeets | J. Vandemeulebroucke | P. Parizel | F. Maes | D. Vandermeulen | P. Suetens | B. Ilsen | J. Mey | D. Robben | N. Buls | Tom Eelbode | A. Snoeckx | Ine Dirks | Panagiotis Gonidakis | S. Tilborghs | S. Willems | J. Bertels | Lucas Fidon | Arne Brys | A. Dubbeldam
[1] James S. Duncan,et al. Medical Image Analysis , 1999, IEEE Pulse.
[2] H Page McAdams,et al. "Crazy-paving" pattern at thin-section CT of the lungs: radiologic-pathologic overview. , 2003, Radiographics : a review publication of the Radiological Society of North America, Inc.
[3] King-Sun Fu,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[5] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[6] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[7] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[8] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[9] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[10] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[11] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[12] Tianqi Chen,et al. Training Deep Nets with Sublinear Memory Cost , 2016, ArXiv.
[13] Sébastien Ourselin,et al. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks , 2017, BrainLes@MICCAI.
[14] Sébastien Ourselin,et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.
[15] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Hao Chen,et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..
[17] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[18] Daguang Xu,et al. Automatic Liver Segmentation Using an Adversarial Image-to-Image Network , 2017, MICCAI.
[19] Konstantinos Kamnitsas,et al. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.
[20] Nikos Paragios,et al. AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation , 2018, MICCAI.
[21] et al.,et al. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.
[22] Spyridon Bakas,et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2018, Lecture Notes in Computer Science.
[23] Andrew L Beers,et al. ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI , 2018, Front. Neurol..
[24] Nima Tajbakhsh,et al. UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.
[25] D. Vandermeulen,et al. Optimization with soft Dice can lead to a volumetric bias , 2019, BrainLes@MICCAI.
[26] Geoffrey E. Hinton,et al. Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.
[27] Hao Tang,et al. Automatic Pulmonary Lobe Segmentation Using Deep Learning , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[28] Dorin Comaniciu,et al. Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Stefano Palmucci,et al. Chest imaging using signs, symbols, and naturalistic images: a practical guide for radiologists and non-radiologists , 2019, Insights into imaging.
[30] Matthew B. Blaschko,et al. Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice , 2019, MICCAI.
[31] Distributionally Robust Deep Learning using Hardness Weighted Sampling. , 2020, 2001.02658.
[32] Bram van Ginneken,et al. CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation , 2020, Radiology.
[33] C. Zheng,et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) , 2020 .
[34] D.-P. Fan,et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.
[35] Richard T. Ellison,et al. Update on COVID-19 , 2020 .
[36] S. Röhrich,et al. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem , 2020, European Radiology Experimental.
[37] B. Song,et al. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review , 2020, European Radiology.
[38] Nikos Paragios,et al. AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia , 2020, medRxiv.
[39] Bram van Ginneken,et al. Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans , 2020, IEEE Transactions on Medical Imaging.
[40] S. Balakrishnan,et al. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. , 2020, AJR. American journal of roentgenology.
[41] Liyuan Liu,et al. On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.
[42] Fergus Gleeson,et al. COVID-19 patients and the radiology department – advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI) , 2020, European Radiology.
[43] Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT , 2020, 2004.01279.
[44] Georg Langs,et al. Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem , 2020, ArXiv.
[45] Thomas J. Re,et al. Quantification of Tomographic Patterns associated with COVID-19 from Chest CT , 2020, ArXiv.
[46] Li Chen,et al. COVID-19 CT Lung and Infection Segmentation Dataset , 2020 .
[47] 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.
[48] J. Jacob,et al. An update on COVID-19 for the radiologist - A British society of Thoracic Imaging statement , 2020, Clinical Radiology.
[49] Kunwei Li,et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19) , 2020, European Radiology.
[50] S. P. Morozov,et al. MosMedData: Chest CT Scans with COVID-19 Related Findings , 2020, medRxiv.
[51] W. Liang,et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.
[52] Wei Chen,et al. The role of CT for Covid-19 patient's management remains poorly defined. , 2020, Annals of translational medicine.
[53] Lian-lian Wu,et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study , 2020, medRxiv.
[54] Ling Shao,et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.
[55] C. Zheng,et al. Time Course of Lung Changes On Chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia , 2020, Radiology.
[56] 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.
[57] Nicola Sverzellati,et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia , 2020, Radiology.
[58] Dinggang Shen,et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.
[59] A. D. Salman. COVID-19 PATIENTS , 2021 .
[60] Ming-Ming Cheng,et al. JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation , 2020, IEEE Transactions on Image Processing.
[61] D. Dong,et al. The Role of Imaging in the Detection and Management of COVID-19: A Review , 2020, IEEE Reviews in Biomedical Engineering.