Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)


Open access medical content databases such as PubMed Central and TCGA offer possibilities to obtain large amounts of images for training deep learning models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classification tasks. In this paper, a CNN-based regression approach to learn the magnification of histopathology images is presented, comparing two deep learning architectures tailored to regress the magnification. A comparison of the performance of the models is done in a dataset of 34,441 breast cancer patches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.


  1. 1.
    Bayramoglu, N., Kannala, J., Heikkilä, J.: Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2440–2445. IEEE (2016)Google Scholar
  2. 2.
    Cruz-Roa, A., et al.: Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017)CrossRefGoogle Scholar
  3. 3.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  4. 4.
    Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7, 29 (2016)CrossRefGoogle Scholar
  5. 5.
    Jimenez-del-Toro, O., Otálora, S., Atzori, M., Müller, H.: Deep multimodal case–based retrieval for large histopathology datasets. In: Wu, G., Munsell, B.C., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds.) Patch-MI 2017. LNCS, vol. 10530, pp. 149–157. Springer, Cham (2017). Scholar
  6. 6.
    Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)CrossRefGoogle Scholar
  7. 7.
    Litjens, G., et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)CrossRefGoogle Scholar
  8. 8.
    Otálora, S., Perdomo, O., Atzori, M., Andresson, M., Hedlund, M., Müller, H.: Determining the scale of image patches using a deep learning approach. In: IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE, April 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Geneva (UNIGE)GenevaSwitzerland
  2. 2.University of Applied Sciences Western Switzerland (HES-SO)SierreSwitzerland

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