Fetching the paper…
Reading the bibliography…
Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain.
1906
Earlier work this paper cites.
Y. LeCun, B. E. Boser, J. S. Denker et al. , “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems , 1990, pp. 396–404
1990
Earlier work this paper cites.
T. M. Mitchell, Machine Learning , 1st ed. USA: McGraw-Hill, Inc., 1997
1997
Earlier work this paper cites.
S. V. Stehman, “Selecting and interpreting measures of thematic classification accuracy,” Remote sensing of Environment , vol. 62, no. 1, pp. 77–89, 1997
1997
Earlier work this paper cites.
S. Aksoy and R. M. Haralick, “Feature normalization and likelihood-based similarity measures for image retrieval,” Pattern recognition letters , vol. 22, no. 5, pp. 563–582, 2001
2001
Earlier work this paper cites.
Y. Yao, L. Rosasco, and A. Caponnetto, “On early stopping in gradient descent learning,” Constructive Approximation , vol. 26, no. 2, pp. 289–315, 2007
2007
Earlier work this paper cites.
S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in 2009 IEEE 12th international conference on computer vision . IEEE, 2009, pp. 1–8
2009
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams et al. , “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2012
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 , vol. 4, no. 2, pp. 26–31, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml , vol. 30, no. 1, 2013, p. 3
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza et al. , “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Merkel, “Docker: lightweight linux containers for consistent development and deployment,” Linux journal , vol. 2014, no. 239, p. 2, 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
B. H. Menze, A. Jakab, S. Bauer et al. , “The multi-modal brain tumor image segmentation benchmark (brats),” IEEE Transactions on Medical Imaging , vol. 34, no. 10, pp. 1993–2024, Oct 2015
2015
Cited alongside, same era.
M. Abadi, A. Agarwal, P. Barham et al. , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
2015
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky et al. , “Improved training of wasserstein gans,” in Advances in neural information processing systems , 2017, pp. 5767–5777
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
J. Jiang, Y.-C. Hu, N. Tyagi et al. , “Tumor-aware, adversarial domain adaptation from ct to mri for lung cancer segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 , A. F. Frangi, J. A. Schnabel, C. Davatzikos et al. , Eds. Cham: Springer International Publishing, 2018, pp. 777–785
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
2016
Cited alongside, same era.
M. Havaei, N. Guizard, N. Chapados, and Y. Bengio, “Hemis: Hetero-modal image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 , S. Ourselin, L. Joskowicz, M. R. Sabuncu et al. , Eds. Cham: Springer International Publishing, 2016, pp. 469–477
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Havaei, A. Davy, D. Warde-Farley et al. , “Brain tumor segmentation with deep neural networks,” Medical image analysis , vol. 35, pp. 18–31, 2017
2017
Cited alongside, same era.
K. Kamnitsas, C. Ledig, V. F. Newcombe et al. , “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical image analysis , vol. 36, pp. 61–78, 2017
2017
Cited alongside, same era.
Y. Wang, C. Wu, L. Herranz et al. , “Transferring gans: Generating images from limited data,” in ECCV , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Yu, L. Zhou, L. Wang et al. , “3d cgan based cross-modality mr image synthesis for brain tumor segmentation,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) , April 2018, pp. 626–630
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu et al. , “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Motamed, I. Gujrathi, D. Deniffel et al. , “A transfer learning approach for automated segmentation of prostate whole gland and transition zone in diffusion weighted mri,” 2019
2019
Closest in time.
2019
Closest in time.
C. Ge, I. Y. Gu, A. Store Jakola, and J. Yang, “Cross-modality augmentation of brain mr images using a novel pairwise generative adversarial network for enhanced glioma classification,” in 2019 IEEE International Conference on Image Processing (ICIP) , Sep. 2019, pp. 559–563
2019
Closest in time.