Page 229 - 《软件学报》2021年第5期

宋杰 等:基于深度学习的数字病理图像分割综述与展望 1453

[49] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proc. of the Int’l
Conf. on Neural Information Processing Systems. 2012. 1097−1105.
[50] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556,
2015.
[51] He KM, Zhang XY, Ren SP, Sun J. Deep residual learning for image recognition. In: Proc. of the IEEE Computer Society Conf.
on Computer Vision and Pattern Recognition. 2016. 770−778.
[52] Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with
convolutions. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. 2015. 1−9.
[53] Howard AG, Zhu ML, Chen B, Kalenichenko D, Wang WJ, Weyand T, Andreetto M, Adam H. MobileNets: Efficient
convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
[54] Huang G, Liu Z, van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proc. of the IEEE Computer
Society Conf. on Computer Vision and Pattern Recognition. 2017. 4700−4708.
[55] Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing coadaptation
of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
[56] Liu W, Rabinovich A, Berg AC. ParseNet: Looking wider to see better. arXiv preprint arXiv:1506.04579, 2015.
[57] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Proc. of the Int’l Conf.
on Medical Image Computing and Computer Assisted Intervention. 2015. 234−241.
[58] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proc. of the IEEE Computer Society Conf.
on Computer Vision and Pattern Recognition. 2015. 1520−1528.
[59] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017,39(12):2481−2495.
[60] Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. Stacked denoising autoencoders: Learning useful representations in a
deep network with a local denoising criterion. Journal of Machine Learning Research, 2010,11(12):3371−3408.
[61] Kingma DP, Welling M. Auto-encoding variational Bayes. In: Proc. of the Int’l Conf. on Learning Representations. 2014. 1−14.
[62] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997,9(8):1735−1780.
[63] Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using
RNN encoder-decoder for statistical machine translation. In: Proc. of the Conf. on Empirical Methods in Natural Language
Processing. 2014. 1724−1734.
[64] van den Oord A, Kalchbrenner N, Kavukcuoglu K. Pixel recurrent neural networks. In: Proc. of the Int’l Conf. on Int’l Conf. on
Machine Learning. 2016. 1747−1756.
[65] Liang XD, Shen XH, Feng JS, Lin L, Yan SC. Semantic object parsing with graph LSTM. In: Proc. of the European Conf. on
Computer Vision. 2016. 125−143.
[66] Liang XD, Lin L, Shen XH, Feng JS, Yan SC, Xing EP. Interpretable structure-evolving LSTM. In: Proc. of the IEEE Computer
Society Conf. on Computer Vision and Pattern Recognition. 2017. 2175−2184.
[67] Ho J, Ermon S. Generative adversarial imitation learning. In: Proc. of the Conf. on Neural Information Processing Systems. 2016.
4565−4573.
[68] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks.
arXiv preprint arXiv:1511.06434, 2016.
[69] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv preprint arXiv:1701.07875, 2017.
[70] Mirza M, Osindero S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
[71] Abadi M, Agarwal A, Barham P, Brevdo E, Chen ZF, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow
I, Harp A, Irving G, Isard M, Jia YQ, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mane D, Monga R, Moore S, Murray D,
Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viegas F, Vinyals O,
Warden P, Wattenberg M, Wicke M, Yu Y, Zheng XQ. Tensorflow: Large-scale machine learning on heterogeneous distributed
systems. arXiv preprint arXiv:1603.04467, 2016.
[72] Liao XY. Introduction of Deep Learning with PyTorch. Beijing: Publishing House of Electronics Industry, 2017 (in Chinese).