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We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
Mitosis detection in breast cancer histology images with deep neural networks
Cireşan, D.C., et al.: · 2013
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M., et al.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., et al.: · 2014
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K., et al.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., et al.: · 2015
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Stain specific standardization of Whole-Slide histopathological images
Bejnordi, B.E., et al.: · 2016
Earlier work this paper cites.
Group equivariant convolutional networks
Cohen, T.S., et al.: · 2016
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., et al.: · 2016
Cited alongside, same era.
A dataset for breast cancer histopathological image classification
Spanhol, F.A., et al.: · 2016
Cited alongside, same era.
Deep learning for identifying metastatic breast cancer
Wang, D., et al.: · 2016
Cited alongside, same era.
Detecting cancer metastases on gigapixel pathology images
Liu, Y., et al.: · 2017
Cited alongside, same era.
A survey on deep learning in medical image analysis
Litjens, G., et al.: · 2017
Cited alongside, same era.
Harmonic networks: Deep translation and rotation equivariance
Worrall, D.E., et al.: · 2017
Later among the works it cites.
Learning steerable filters for rotation equivariant CNNs
Weiler, M., et al.: · 2017
Later among the works it cites.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
B, E.B., et al.: · 2017
Later among the works it cites.
Supervised intra-embedding of fisher vectors for histopathology image classification
Song, Y., et al.: · 2017
Later among the works it cites.
Robustness of Rotation-Equivariant networks to adversarial perturbations
Dumont, B., et al.: · 2018
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