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Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis.
G. S. Lodwick, T. E. Keats, and J. P. Dorst, “The coding of roentgen images for computer analysis as applied to lung cancer,” Radiology , vol. 81, no. 2, pp. 185–200, 1963
1963
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. K. Gohagan, P. C. Prorok, R. B. Hayes, and B.-S. Kramer, “The prostate, lung, colorectal and ovarian (plco) cancer screening trial of the national cancer institute: history, organization, and status,” Controlled Clin. Trials , vol. 21, no. 6, pp. 251S–272S, 2000
2000
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F.-F. Li, “Imagenet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. IEEE, 2009, pp. 248–255
2009
Earlier work this paper cites.
T. Tieleman and G. Hinton, “Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude,” COURSERA: Neural Networks for Machine Learning, 2012
2012
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. Int. Conf. Mach. Learn , vol. 30, no. 1, 2013, p. 3
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Representations , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Mach. Learn , 2015, pp. 448–456
2015
Earlier work this paper cites.
H. Yang, J. T. Zhou, and J. Cai, “Improving multi-label learning with missing labels by structured semantic correlations,” in Proc. Eur. Conf. Comput. Vis. Springer, 2016, pp. 835–851
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” in Proc. Int. Conf. Learn. Representations , 2017
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual domains with residual adapters,” in Proc. Advances Neural Inf. Process. Syst. , 2017, pp. 506–516
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 4700–4708
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Proc. Advances Neural Inf. Process. Syst. , 2017, pp. 5767–5777
2017
Earlier work this paper cites.
J. R. Zech, M. A. Badgeley, M. Liu, A. B. Costa, J. J. Titano, and E. K. Oermann, “Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study,” PLoS Med. , vol. 15, no. 11, 2018
2018
Earlier work this paper cites.
X. Wang, Y. Peng, L. Lu, Z. Lu, and R. M. Summers, “Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 9049–9058
2018
Earlier work this paper cites.
Y. Tang, X. Wang, A. P. Harrison, L. Lu, J. Xiao, and R. M. Summers, “Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs,” in Int. Workshop on Mach. Learn. Med. Imaging . Springer, 2018, pp. 249–258
2018
Earlier work this paper cites.
Z. Li, C. Wang, M. Han, Y. Xue, W. Wei, L.-J. Li et al. , “Thoracic disease identification and localization with limited supervision,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 8290–8299
2018
Earlier work this paper cites.
S. Guendel, S. Grbic, B. Georgescu, S. Liu, A. Maier, and D. Comaniciu, “Learning to recognize abnormalities in chest x-rays with location-aware dense networks,” in Iberoamerican Congress on Pattern Recognit. Springer, 2018, pp. 757–765
2018
Cited alongside, same era.
B. Wu, F. Jia, W. Liu, B. Ghanem, and S. Lyu, “Multi-label learning with missing labels using mixed dependency graphs,” Int. J. Comput. Vis. , vol. 126, no. 8, pp. 875–896, 2018
2018
Cited alongside, same era.
G. van Tulder and M. de Bruijne, “Learning cross-modality representations from multi-modal images,” IEEE Trans. Med. Imaging , vol. 38, no. 2, pp. 638–648, 2018
2018
Cited alongside, same era.
Y. Zhang, S. Miao, T. Mansi, and R. Liao, “Task driven generative modeling for unsupervised domain adaptation: Application to x-ray image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 599–607
2018
Cited alongside, same era.
T. Durand, N. Mehrasa, and G. Mori, “Learning a deep convnet for multi-label classification with partial labels,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 647–657
2019
Later among the works it cites.
S. Wang, L. Yu, X. Yang, C. W. Fu, and P. A. Heng, “Patch-based output space adversarial learning for joint optic disc and cup segmentation,” IEEE Trans. Med. Imaging , vol. 38, no. 11, pp. 2485–2495, 2019
2019
Later among the works it cites.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, “Mixmatch: A holistic approach to semi-supervised learning,” in Proc. Advances Neural Inf. Process. Syst. , 2019, pp. 5050–5060
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Proc. Advances Neural Inf. Process. Syst. , 2019, pp. 8024–8035
2019
Later among the works it cites.
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Z. Zhang, L. Yang, and Y. Zheng, “Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 9242–9251
2018
Cited alongside, same era.
Z. Chen, V. Badrinarayanan, C.-Y. Lee, and A. Rabinovich, “Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks,” in Proc. Int. Conf. Mach. Learn , 2018, pp. 793–802
2018
Cited alongside, same era.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu et al. , “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” in Proc. Advances Neural Inf. Process. Syst. , 2018, pp. 8527–8537
2018
Cited alongside, same era.
X. Pan, P. Luo, J. Shi, and X. Tang, “Two at once: Enhancing learning and generalization capacities via ibn-net,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 464–479
2018
Cited alongside, same era.
Q. Guan and Y. Huang, “Multi-label chest x-ray image classification via category-wise residual attention learning,” Pattern Recognit. Letters , 2018
2018
Cited alongside, same era.
C. Yan, J. Yao, R. Li, Z. Xu, and J. Huang, “Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays,” in Proc. ACM Int. Conf. Bioinformatics, Computational Biology, and Health Informatics . ACM, 2018, pp. 103–110
2018
Cited alongside, same era.
J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute et al. , “Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,” in Proc. AAAI Conf. Artif. Intel. , 2019, p. 590–597
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. C. Peterson, R. M. Battleday, T. L. Griffiths, and O. Russakovsky, “Human uncertainty makes classification more robust,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 9617–9626
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Lenga, H. Schulz, and A. Saalbach, “Continual learning for domain adaptation in chest x-ray classification,” in Conf. Med. Imag. Deep Learn. , 2020
2020
Closest in time.
N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” Med. Image Analysis , p. 101693, 2020
2020
Closest in time.
X. Li, L. Yu, H. Chen, C. Fu, L. Xing, and P. Heng, “Transformation-consistent self-ensembling model for semisupervised medical image segmentation,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–12, 2020
2020
Closest in time.
Q. Liu, L. Yu, L. Luo, Q. Dou, and P. A. Heng, “Semi-supervised medical image classification with relation-driven self-ensembling model,” IEEE Trans. Med. Imaging , 2020
2020
Closest in time.
X. Wang, F. Tang, H. Chen, L. Luo, Z. Tang, A.-R. Ran et al. , “Ud-mil: Uncertainty-driven deep multiple instance learning for oct image classification,” IEEE J. Biomed. Health Inform. , 2020
2020
Closest in time.
Y. Xia, F. Liu, D. Yang, J. Cai, L. Yu, Z. Zhu et al. , “3d semi-supervised learning with uncertainty-aware multi-view co-training,” in IEEE Winter Conf. Appl. Comput. Vis. , 2020, pp. 3646–3655
2020
Closest in time.
Q. Liu, Q. Dou, L. Yu, and P. A. Heng, “Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data,” IEEE Trans. Med. Imaging , 2020
2020
Closest in time.
C. Chen, Q. Dou, H. Chen, J. Qin, and P. A. Heng, “Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,” IEEE Trans. Med. Imaging , 2020
2020
Closest in time.
J. P. Cohen, M. Hashir, R. Brooks, and H. Bertrand, “On the limits of cross-domain generalization in automated x-ray prediction,” in Conf. Med. Imag. Deep Learn. , 2020
2020
Closest in time.
A. Majkowska, S. Mittal, D. F. Steiner, J. J. Reicher, S. M. McKinney, G. E. Duggan et al. , “Chest radiograph interpretation with deep learning models: Assessment with radiologist-adjudicated reference standards and population-adjusted evaluation,” Radiology , vol. 294, no. 2, pp. 421–431, 2020
2020
Closest in time.
X. Wang, H. Chen, A.-R. Ran, L. Luo, P. P. Chan, C. C. Tham et al. , “Towards multi-center glaucoma oct image screening with semi-supervised joint structure and function multi-task learning,” Med. Image Analysis , vol. 63, p. 101695, 2020
2020
Closest in time.
L. Zhang, X. Wang, D. Yang, T. Sanford, S. Harmon, B. Turkbey et al. , “Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,” IEEE Trans. Med. Imaging , 2020
2020
Closest in time.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 2097–2106
2097
Closest in time.