Domain-adversarial neural networks to address the appearance variability of histopathology images

Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. 23(4), 291–299 (2001)

2001

Earlier work this paper cites.

Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2579–2605 (2008)

2008

Earlier work this paper cites.

Macenko, M., Niethammer, M., Marron, J., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: IEEE ISBI 2009. pp. 1107–1110 (2009)

2009

Earlier work this paper cites.

Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: MICCAI 2013. pp. 411–418 (2013)

2013

Cited alongside, same era.

Veta, M., Van Diest, P.J., Willems, S.M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A.B., Vestergaard, J.S., Dahl, A.B., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis 20(1), 237–248 (2015)

2015

Cited alongside, same era.

Tumor proliferation assessment challenge 2016. http://tupac.tue-image.nl

2016

Cited alongside, same era.

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. Journal of Machine Learning Research 17(59), 1–35 (2016)

2016

Later among the works it cites.

Veta, M., van Diest, P.J., Jiwa, M., Al-Janabi, S., Pluim, J.P.: Mitosis counting in breast cancer: Object-level interobserver agreement and comparison to an automatic method. PloS one 11(8), e0161286 (2016)

2016

Later among the works it cites.

Kamnitsas, K., Baumgartner, C.F., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Nori, A.V., Criminisi, A., Rueckert, D., Glocker, B.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: IPMI, 2017 (2017)

2017

Closest in time.