Abstract
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%).
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References
Coroller, T.P., Grossmann, P., Hou, Y., Velazquez, E.R., Leijenaar, R.T., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R.H., Aerts, H.J.: CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother. Oncol. 114(3), 345–350 (2015)
Aerts, H., Velazquez, E.R., Leijenaar, R., Parmar, C., Grossmann, P., Cavalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 1–8 (2014)
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6), e0177544 (2017)
Chan, J.K.: The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology. Int. J. Surg. Pathol. 22(1), 12–32 (2014)
Meyer, J.S., Alvarez, C., Milikowski, C., Olson, N., Russo, I., Russo, J., Glass, A., Zehnbauer, B.A., Lister, K., Parwaresch, R.: Breast carcinoma malignancy grading by Bloom-Richardson system vs proliferation index: reproducibility of grade and advantages of proliferation index. Modern Pathol. 18(8), 1067 (2005)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567. IEEE (2016)
Cao, H., Bernard, S., Heutte, L., Sabourin, R.: Dissimilarity-based representation for radiomics applications. arXiv preprint arXiv:1803.04460 (2018)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)
Hamilton, N.A., Pantelic, R.S., Hanson, K., Teasdale, R.D.: Fast automated cell phenotype image classification. BMC Bioinform. 8(1), 110 (2007)
Coelho, L.P.: Mahotas: open source software for scriptable computer vision. J. Open Res. Softw. 1 (2013)
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)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995. IEEE (2017)
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012 (2017)
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Biau, G., Scornet, E.: A random forest guided tour. Test 25(2), 197–227 (2016)
Bill, J., Fokoué, E.: A comparative analysis of predictive learning algorithms on high-dimensional microarray cancer data. Serdica J. Comput. 8(2), 137–168 (2014)
Acknowledgment
This work is part of the DAISI project, co-financed by the European Union with the European Regional Development Fund (ERDF) and by the Normandy Region.
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Cao, H., Bernard, S., Heutte, L., Sabourin, R. (2018). Improve the Performance of Transfer Learning Without Fine-Tuning Using Dissimilarity-Based Multi-view Learning for Breast Cancer Histology Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_88
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DOI: https://doi.org/10.1007/978-3-319-93000-8_88
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