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Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation.
arXiv preprint arXiv:1905.12120 (2019b)
Hatamizadeh, A., Hosseini, H., Liu, Z., Schwartz, S.D., Terzopoulos, D.: Deep dilated convolutional nets for the automatic segmentation of retinal vessels · 1905
Earlier work this paper cites.
arXiv preprint arXiv:1908.06933 (2019a)
Hatamizadeh, A., Hoogi, A., Sengupta, D., Lu, W., Wilcox, B., Rubin, D., Terzopoulos, D.: Deep active lesion segmentation · 1908
Earlier work this paper cites.
In: Proc. MICCAI. LNCS, vol. 9351, pp. 234–241 (2015)
Ronneberger, O., P.Fischer, Brox, T.: U-net: Convolutional networks for biomedical image segmentation · 2015
Earlier work this paper cites.
In: Fourth International Conference on 3D Vision (3DV) (2016)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation · 2016
Earlier work this paper cites.
Scientific Data 4 (2017)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features · 2017
Cited alongside, same era.
In: CVPR (2017)
Yu, Z., Feng, C., Liu, M., Ramalingam, S.: Casenet: Deep category-aware semantic edge detection · 2017
Cited alongside, same era.
In: BrainLes, Medical Image Computing and Computer Assisted Intervention (MICCAI). pp. 311–320. LNCS, Springer (2018)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization · 2018
Cited alongside, same era.
In: European Conference on Computer Vision (ECCV) (2018)
Yu, Z., Liu, W., Zou, Y., Feng, C., Ramalingam, S., Vijaya Kumar, B., Kautz, J.: Simultaneous edge alignment and learning · 2018
Cited alongside, same era.
In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Acuna, D., Kar, A., Fidler, S.: Devil is in the edges: Learning semantic boundaries from noisy annotations · 2019
Closest in time.
In: International Conference on Learning Representations (ICLR) (2019)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness · 2019
Closest in time.
https://arxiv.org/abs/1906.00590 (2019)
Hu, Y., Zou, Y., Feng, J.: Panoptic edge detection · 2019
Closest in time.
arXiv preprint arXiv:1907.05740 (2019)
Takikawa, T., Acuna, D., Jampani, V., Fidler, S.: Gated-scnn: Gated shape cnns for semantic segmentation · 2019
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