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Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET scans.
Active shape models-their training and application
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Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
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L. Li, Y. Zheng, M. Kallergi, and R. A. Clark · 2001
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M. Loog and B. van Ginneken · 2002
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Active shape model segmentation with optimal features
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Automatic detection of abnormalities in chest radiographs using local texture analysis
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Shape regularized active contour using iterative global search and local optimization
T. Yu, J. Luo, and N. Ahuja · 2005
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Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification
M. Loog and B. Ginneken · 2006
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Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
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Minimal shape and intensity cost path segmentation
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Y. Shi, F. Qi, Z. Xue, L. Chen, K. Ito, H. Matsuo, and D. Shen · 2008
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A region based active contour method for x-ray lung segmentation using prior shape and low level features
P. Annangi, S. Thiruvenkadam, A. Raja, H. Xu, X. Sun, and L. Mao · 2010
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An edge-region force guided active shape approach for automatic lung field detection in chest radiographs
T. Xu, M. Mandal, R. Long, I. Cheng, and A. Basu · 2012
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An edge-region force guided active shape approach for automatic lung field detection in chest radiographs
T. Xu, M. Mandal, R. Long, I. Cheng, and A. Basu · 2012
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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N. England · 2016
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Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration
S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, and C. J. McDonald · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, A. van den Hengel, and I. Reid · 2016
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Semantic segmentation using adversarial networks
P. Luc, C. Couprie, S. Chintala, and J. Verbeek · 2016
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Clinical radiology uk workforce census 2014 report, September 2016
T. R. C. of Radiologists · 2016
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Clinical radiology uk workforce census 2015 report, September 2016
T. R. C. of Radiologists · 2016
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Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation
H.-C. Shin, K. Roberts, L. Lu, D. Demner-Fushman, J. Yao, and R. M. Summers · 2016
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