Abstract
Convolutional neural networks (CNN) have shown to be effective in medical image processing and analysis. Herein, we propose a CNN approach to perform patch- and pixel-wise histology labeling on breast microscopy and whole slide images (WSI), respectively. We devise a processing block that is capable of extracting compact features in an efficient manner. Based upon the processing block, classification and segmentation networks are built. Two networks share an encoder via partial transformation and transfer learning to maximally utilize the trained network and available dataset. 400 microscopy images and 10 WSI were employed to evaluate the proposed approach. For patch classification, an accuracy of 71% and 65% were obtained on the training and testing dataset, respectively. As for segmentation, we achieved an overall score of 0.7343 and 0.4945 on the training and testing dataset, respectively.
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References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA Cancer J. Clin. 66, 7–30 (2016)
Elmore, J.G., Longton, G.M., Carney, P.A., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313, 1122–1132 (2015)
Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170–175 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., Heng, P.A.: DCAN: deep contour-aware networks for object instance segmentation from histology images. Med. Image Anal. 36, 135–146 (2017)
Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. Med. Image Comput. Comput. Assist. Interv. 16, 411–418 (2013)
Cruz-Roa, A.A., Arevalo Ovalle, J.E., Madabhushi, A., Gonzalez Osorio, F.A.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. Med. Image Comput. Comput. Assist. Interv. 16, 403–410 (2013)
Litjens, G., Sanchez, C.I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen-van de Kaa, C., Bult, P., van Ginneken, B., van der Laak, J.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)
Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. ArXiv e-prints, vol. 1504 (2015)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. ArXiv e-prints, vol. 1608 (2016)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. ArXiv e-prints, vol. 1511 (2015)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. ArXiv e-prints, vol. 1709 (2017)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. ArXiv e-prints, vol. 1611 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. ArXiv e-prints, vol. 1603 (2016)
Dong, X., Kang, G., Zhan, K., Yang, Y.: EraseReLU: a simple way to ease the training of deep convolution neural networks. ArXiv e-prints, vol. 1709 (2017)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. ArXiv e-prints, vol. 1511 (2015)
Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. ArXiv e-prints, vol. 1611 (2016)
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large Kernel matters – improve semantic segmentation by global convolutional network. ArXiv e-prints, vol. 1703 (2017)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. ArXiv e-prints, vol. 1409 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. ArXiv e-prints, vol. 1502 (2015)
Acknowledgments
This study is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1C1B2012433).
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Vu, Q.D., To, M.N.N., Kim, E., Kwak, J.T. (2018). Micro and Macro Breast Histology Image Analysis by Partial Network Re-use. 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_102
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DOI: https://doi.org/10.1007/978-3-319-93000-8_102
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