Built on

Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a” siamese” time delay neural network. In: Advances in neural information processing systems. pp. 737–744 (1994)
1994
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics 7
2016
Earlier work this paper cites.
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Original
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European conference on computer vision. pp. 649–666. Springer (2016)
2016
Earlier work this paper cites.

Similar

Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694 (2017)
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2017
Cited alongside, same era.
Xue, Y., Ray, N.: Cell detection in microscopy images with deep convolutional neural network and compressed sensing. arXiv preprint arXiv:1708.03307 (2017)
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Campanella, G., Silva, V.W.K., Fuchs, T.J.: Terabyte-scale deep multiple instance learning for classification and localization in pathology. arXiv preprint arXiv:1805.06983 (2018)
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Cited alongside, same era.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)
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2018
Cited alongside, same era.

Then

Lee, B., Paeng, K.: A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 841–850. Springer (2018)
2018
Later among the works it cites.
McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
Original
2018
Later among the works it cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3733–3742 (2018)
2018
Later among the works it cites.
Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: A survey. arXiv preprint arXiv:1902.06162 (2019)
Original
2019
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2019

Self-Supervised Similarity Learning for Digital Pathology

Gildenblat, Jacob, Klaiman, Eldad

Understand

Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology.

However it presents the downside of missing domain specific image information.
In digital pathology, supervised training data is expensive and difficult to collect.
We propose a self-supervised method for feature extraction by similarity learning on whole slide images (WSI) that is simple to implement and allows creation of robust and compact image descriptors.

Built on

Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a” siamese” time delay neural network. In: Advances in neural information processing systems. pp. 737–744 (1994)
1994
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics 7
2016
Earlier work this paper cites.
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Original
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European conference on computer vision. pp. 649–666. Springer (2016)
2016
Earlier work this paper cites.

Similar

Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694 (2017)
Original
2017
Cited alongside, same era.
Xue, Y., Ray, N.: Cell detection in microscopy images with deep convolutional neural network and compressed sensing. arXiv preprint arXiv:1708.03307 (2017)
Original
2017
Cited alongside, same era.
Campanella, G., Silva, V.W.K., Fuchs, T.J.: Terabyte-scale deep multiple instance learning for classification and localization in pathology. arXiv preprint arXiv:1805.06983 (2018)
Original
2018
Cited alongside, same era.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)
Original
2018
Cited alongside, same era.

Then

Lee, B., Paeng, K.: A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 841–850. Springer (2018)
2018
Later among the works it cites.
McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
Original
2018
Later among the works it cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3733–3742 (2018)
2018
Later among the works it cites.
Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: A survey. arXiv preprint arXiv:1902.06162 (2019)
Original
2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Lahiani, A., Gildenblat, J., Klaman, I., Albarqouni, S., Navab, N., Klaiman, E.: Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach. arXiv preprint arXiv:1810.06415 (2018)

Original

2018

Cited alongside, same era.

Lahiani, A., Gildenblat, J., Klaman, I., Navab, N., Klaiman, E.: Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks. IET Image Processing (2019)

2019

Closest in time.