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This paper proposes a novel framework for lung segmentation in chest X-rays.
Development of a digital image database for chest radiographs with and without a lung nodule
J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi · 2000
Earlier work this paper cites.
Improved method for automatic identification of lung regions on chest radiographs
L. Li, Y. Zheng, M. Kallergi, and R. A. Clark · 2001
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Two public chest X-ray datasets for computer-aided screening of pulmonary diseases
S. Jaeger, S. Candemir, Y. X. Antani, S.and Wang, P. X. Lu, and G. Thoma · 2014
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected CRFs
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool
A. A. Taha and A. Hanbury · 2015
Cited alongside, same era.
Biomedical Image Segmentation: Advances and Trends
A. El-Baz, X. Jiang, and J.S. Suri · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Scan: Structure correcting adversarial network for chest x-rays organ segmentation
ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers · 2017
Later among the works it cites.
Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: Slice-propagated 3D mask generation from 2D RECIST
J. Cai, Y. Tang, L. Lu, A. P Harrison, K. Yan, J. Xiao, L. Yang, and R. M. Summers · 2018
Later among the works it cites.
Drinet for medical image segmentation
L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert · 2018
Later among the works it cites.
CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation
D. Jin, Z. Xu, Y. Tang, A. P. Harrison, and D. J. Mollura · 2018
Later among the works it cites.
Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large-scale lesion database
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W. Dai, J. Doyle, X. Liang, H. Zhang, N. Dong, Y. Li, and E. P. Xing · 2017
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2017
Cited alongside, same era.
Multimodal unsupervised image-to-image translation
X. Huang, M.Y. Liu, S. Belongie, and J. Kautz
Cited in the paper.
CCNet: Criss-cross attention for semantic segmentation
Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu
Cited in the paper.
CT image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement
Y. Tang, J. Cai, L. Lu, A. P. Harrison, K. Yan, J. Xiao, L. Yang, and R. M. Summers
Cited in the paper.
Semi-automatic RECIST labeling on CT scans with cascaded convolutional neural networks
Y. Tang, A. P. Harrison, M. Bagheri, J. Xiao, and R. M. Summers
Cited in the paper.
Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs
Y. Tang, X. Wang, A. P. Harrison, L. Lu, J. Xiao, and R. M. Summers
Cited in the paper.
K. Yan, X. Wang, L. Lu, L. Zhang, A. P. Harrison, M. Bagheri, and R. M. Summers · 2018
Later among the works it cites.
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
K. Yan, Y. Peng, Z. Lu, and R. M. Summers · 2019
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