Archives

  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br Fig Comparison of sensitivity uses

    2020-08-18

    
    Fig. 8. Comparison of sensitivity uses the same method across different data-sets. The blue and green bars show the average sensitivity of image-wise results using ‘Google's Inception-V3 + SVM’ method on the Bioimaging2015 dataset and our dataset, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
    Therefore, to accurately classify benign images, more adequate dataset volumes and data D AP5 are needed to train the algorithm.
    6. Conclusion
    In this article, we proposed a new method for breast cancer pa-thological image classification using a hybrid convolutional and re-current deep neural network. Based on the richer feature representation of the pathological image patches, our method considered the short-term and the long-term spatial correlations between patches through a RNN, which is right behind a richer multilevel CNN feature extractor. Thus, the short-term and long-term spatial correlations between patches were both considered. Through extensive experiments and compar-isons, it was shown that our new method outperforms the state-of-the-art method. Additionally, we released a larger and more diverse dataset of breast cancer pathological images to the scientific community. We hope that the dataset can serve as a benchmark to facilitate a broader study of deep learning in the field of breast cancer pathologic images.
    For the future work, to improve the accuracy of classification, outstanding deep learning algorithms and large enough as well as di-verse dataset are indispensable. In terms of algorithms, the use of at-tention mechanisms in deep learning algorithms is a direction that can be tried, because it has achieved outstanding performance in natural image processing. In terms of dataset, larger dataset should be opened like ImageNet to provide a benchmark for the research community. Of course, advances in hardware are equally important. After all, it is ideal to directly use a complete high-resolution image as input to a deep neural network. At the same time, we are trying to extend this approach to whole slide images which will more difficult but will produce greater value in clinical practice.
    Acknowledgements
    References
    [11] P. Filipczuk, T. Fevens, A. Krzyzak, R. Monczak, Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies, IEEE Trans. Med. Imaging 32 (2013) 2169–2178. [12] Y. Zhang, B. Zhang, F. Coenen, W. Lu, Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles, Mach. Vis. Appl. 24 (2013) 1405–1420. [13] Y. Zhang, B. Zhang, F. Coenen, J. Xiao, W. Lu, One-class kernel subspace ensemble for medical image classification, EURASIP J Adv Sign Process 07 (2014) 1–13. [14] A.D. Belsare, M.M. Mushrif, M.A. Pangarkar, N. Meshram, Classification of artificial selection breast cancer histopathology images using texture feature analysis, in: TENCON 2015–2015 IEEE Region 10 Conference, 2016, pp. 1–5. [15] B. Zhang, Breast cancer diagnosis from biopsy images by serial fusion of random subspace ensembles, in: International Conference on Biomedical Engineering and Informatics, 2011, pp. 180–186. [16] A. Cruzroa, A. Basavanhally, F. González, H. Gilmore, M. Feldman, S. Ganesan, N. Shih, J. Tomaszewski, A. Madabhushi, Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural network, Proceedings of SPIE – The International Society for Optical Engineering 9041, 2014, 139–144.
    [18] N. Bayramoglu, J. Kannala, J. Heikkilä, Deep learning for magnification in-dependent breast cancer histopathology image classification, in: International Conference on Pattern Recognition, 2017, pp. 2440–2445. [19] T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Polónia,
    M. Prastawa, M. Chan, et al., Bach: Grand challenge on breast cancer histology images, Med. Image Anal. (2019) In press. [21] S. Vesal, N. Ravikumar, A.A. Davari, S. Ellmann, A. Maier, Classification of breast cancer histology images using transfer learning, in: International Conference Image Analysis and Recognition, 2018, pp. 812–819.  Methods xxx (xxxx) xxx–xxx
    [22] Y.S. Vang, Z. Chen, X. Xie, Deep learning framework for multi-class breast cancer histology image classification, in: International Conference Image Analysis and Recognition, 2018, pp. 914–922.
    [23] A. Rakhlin, A. Shvets, V. Iglovikov, A.A. Kalinin, Deep convolutional neural net-works for breast cancer histology image analysis, in: International Conference Image Analysis and Recognition, 2018, pp. 737–744. [24] R. Awan, N.A. Koohbanani, M. Shaban, A. Lisowska, N. Rajpoot, Context-aware learning using transferable features for classification of breast cancer histology images, in: International Conference Image Analysis and Recognition, 2018, pp. 788–795.