nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440

2015

Earlier work this paper cites.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015

Original

2015

Earlier work this paper cites.

B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest et al. , “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE TMI , vol. 34, no. 10, pp. 1993–2024, 2015

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in ECCV . Springer, 2016, pp. 630–645

2016

Earlier work this paper cites.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in International Conference on 3D Vision . IEEE, 2016, pp. 565–571

2016

Earlier work this paper cites.

D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Instance normalization: The missing ingredient for fast stylization,” arXiv preprint arXiv:1607.08022 , 2016

Original

2016

Cited alongside, same era.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Deep Learning and Data Labeling for Medical Applications . Springer, 2016, pp. 179–187

2016

Cited alongside, same era.

F. Isensee, P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein, “Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge,” in International MICCAI Brainlesion Workshop . Springer, 2017, pp. 287–297

2017

Cited alongside, same era.

X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P. A. Heng, “H-DenseUNet: Hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes,” arXiv preprint arXiv:1709.07330 , 2017

Original

2017

Cited alongside, same era.

F. Isensee, P. F. Jaeger, P. M. Full, I. Wolf, S. Engelhardt, and K. H. Maier-Hein, “Automatic cardiac disease assessment on cine-mri via time-series segmentation and domain specific features,” in International Workshop on Statistical Atlases and Computational Models of the Heart . Springer, 2017, pp. 120–129

2017

Later among the works it cites.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Springer, 2017, pp. 240–248

2017

Later among the works it cites.

A. G. Roy, N. Navab, and C. Wachinger, “Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks,” arXiv preprint arXiv:1803.02579 , 2018

Original

2018

Closest in time.

O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz et al. , “Attention U-Net: Learning Where to Look for the Pancreas,” arXiv preprint arXiv:1804.03999 , 2018