Keyword [SPADE]

Jiang L, Zhou Z, Leung T, et al. MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels[J]. arXiv preprint arXiv:1712.05055, 2017.

Radosavovic I, Dollár P, Girshick R, et al. Data distillation: Towards omni-supervised learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 4119-4128.

Keyword [FPN]

Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 2117-2125.

Keyword [Focal Loss]

Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988.

Liu M Y, Tuzel O. Coupled generative adversarial networks[C]//Advances in neural information processing systems. 2016: 469-477.

Keyword [Multi-Scale DenseNet]

Huang G, Chen D, Li T, et al. Multi-scale dense convolutional networks for efficient prediction[J]. arXiv preprint arXiv:1703.09844, 2017, 2.

Keyword [MRI]

Yu L, Yang X, Chen H, et al. Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images[C]//AAAI. 2017: 66-72

Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis[J]. arXiv preprint arXiv:1702.05747, 2017.

Lee J G, Jun S, Cho Y W, et al. Deep Learning in Medical Imaging: General Overview[J]. Korean Journal of Radiology, 2017, 18(4): 570-584.

Kim J, Kwon Lee J, Mu Lee K. Deeply-recursive convolutional network for image super-resolution[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1637-1645.