Scientific Journal

Herald of Advanced Information Technology


Nowadays, accurate diagnosis of diseases, their treatment and prognosis is a very acute problem of modern medicine. By studying information about human proteins, you can identify differentially expressed proteins. These proteins are potentially interesting biomarkers that can be used for an accurate diagnosis, prognosis, or selection of individual treatments, especially for cancer. A surprising finding from this research is that we have relatively few proteins that are tissue specific. Almost half of all proteins are categorized as housekeeping proteins, expressed in all cells. Only 2,300 proteins in the human body have been identified as tissue enriched, meaning they have elevated expression levels in certain tissues. Thanks to advances in high-throughput microscopy, images are generated too quickly for manual evaluation. Consequently, the need for automating the analysis of biomedical images is as great as ever to speed up the understanding of human cells and diseases. Historically, the classification of proteins was limited to individual patterns in one or more cell types, but in order to fully understand the complexity of a human cell, models must classify mixed patterns according to a number of different human cells. The article formulates the problem of image classification in medical research. In this area, classification methods using deep convolutional neural networks are actively used. Presented article gives a brief overview of the various approaches and methods of similar research. As a dataset was taken “The Human Protein Atlas”, that presents a tissue-based map of the human proteome, completed in 2014 after 11 years of research. All protein expression profiling data is publicly accessible in an interactive database, enabling tissue-based exploration of the human proteome. It was done an analysis of the work and the methods that were used during the research. To solve this problem, the deep neural network model is proposed taking into account the characteristics of the domain and the sample under study. The neural network model is based on Inception-v3 architecture. Optimization procedure contains combination of several tweaks for fast convergence: stochastic gradient descent with warm restarts (learning rate schedule for exploring different local minima), progressive image resizing (training starts from small resolution and sequentially increases each cycle of SGDR). We propose new method for threshold selection for F1 measure. Developed model can be used to create an instrument integrated into the medical system of intellectual microscopy to determine the location of the protein from a high-performance image.

  1. (2018). “Human Protein Atlas Image Classification”. [Electronic source] – Availableat : – Active link : (11.03.2019).
  2. (2018). “ Protein Atlas Image Classification”. 12-th place solution. [Electronic source]. –  Availableat: –Active link : (11.03.2019).
  3. Andre, Esteva, Brett, Kuprel, Rob, Novoa, Justin, Ko, Susan, Swetter, Helen, Blau, & Sebastian, Thrun. (2017). “Dermatologist-level Classification of Skin Cancer with Deep Neural Networks”. Nature Publ., pp. 115-118.
  4. Christian, Szegedy, Vincent, Vanhoucke, Sergey, Ioffe, Jon, Shlens & Zbigniew, Wojna. (2016). “Rethinking the Inception Architecture for Computer Vision”. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818-2826.
  5. (2018). Cubuk. Ekin, Barret. Zoph et al. “Auto Augment: Learning Augmentation Policies from Data – in proceedings of Computer Vision and Pattern Recognition”site.
  6. Elad. Eban, Mariano. Schain, Alan. Mackey, Ariel. Gordon, Rif A. Saurous & Gal. Elidan. (2017). “Scalable Learning of Non-Decomposable Objectives”. In arXiv preprint arXiv: 1608.04802v2, pp. 1-7 site.
  7. Elisabeth. Rumetshofer, Markus, Hofmarcher, Clemens, Röhrl, Sepp, Hochreiter & Günter, Klambauer. (2018). „Human-level Protein Localization with Convolutional Neural Networks”. ICLR 2019 Conference Blind Submission, pp. 221-232.
  8. Godinez, W. J., Hossain, I., Lazic, S. E., Davies, J. W., & Zhang, X. (2019). “A multi-scale convolutional neural network for phenotyping high-content cellular images”. Bioinformatics, 33(13), pp. 2010-2019. DOI:10.1093/bioinformatics/btx069.
  9. Huang, Gao & Zhuang, Liu. (2017). “Densely Connected Convolutional Networks - In proceeding of Computer Vision and Pattern Recognition”, pp. 1-77. DOI: 10.1109/CVPR.2017.243.
  10. Ilya, Loshchilov & Frank, Hutter. (2017). “SGDR: Stochastic Gradient Descent with Warm Restarts”. In ICLR 2017 proceedings,
  11. Kraus, O. Z., Ba, J. L., & Frey, B. J. (2017). “Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics”, 32(12), pp. 52-59. DOI: 10.1093/bioinformatics/btw252.
  12. Latt, S. A., Stetten, G., Juergens, L. A., Willard, H. F., & Scher, C.D (July 1975). Recent developments in the detection of deoxyribonucleic acid synthesis by 33258 Hoechst fluorescence”. Journal of Histochemistry and Cytochemistry. 23 (7): pp. 493-505, doi:10.1177/23.7.1095650.
  13. Maximilian, Ilse, Jakub, M. Tomczak & Max, Welling. (2018). “Attention-based Deep Multiple Instance Learning”. In arXiv preprint arXiv:1802.04712v4,
  14. (2013). Niwas, S, Issac, Kårsnäs, er al. „Automated classification of immunostaining patterns in breast tissue from the human protein Atlas”. Journal of pathology informatics. 4. S14. DOI: 10.4103/2153-3539.109881.
  15. Sechidis, K., Tsoumakas, G. & Vlahavas, I. (2011). “On the Stratification of Multi-Label Data. Machine Learning and Knowledge Discovery in Databases”. ECML PKDD 2011. Lecture Notes in Computer Science, Vol. 6913. Springer, Berlin, Heidelberg, pp. 145-158. DOI: 10.1007/978-3-642-23808-6_10.
  16. (2018). Sullivan, D, Casper, F. Winsnes, et al. “Deep learning is combined with massive-scale citizen science to improve large-scale image classification”, Nature Biotechnology Publ., Vol. 36, pp. 820-828. DOI: 10.1038/nbt.4225.
  17. (2017). Thul, P. J, Åkesson, L er al. „A subcellular map of the human proteome”.Science. 356(6340): eaal3321 PubMed: 28495876. DOI: 10.1126/science.aal3321.
  18. (2019). “The multilocalizing proteome”. [Electronic source]. – Availableat: – –Active link : 11.03.2019.
  19. Vincent, Vanhoucke, Christian, Szegedy, Sergey, Ioffe, Jonathon, Shlens & Zbigniew, Wojna. (2015). “Rethinking the Inception Architecture for Computer Vision”. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9.
  20. (2016). Wenbin, Li, Jing, Huo, Yinghuan, Shi et al. “Online Deep Metric Learning”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11-19.
  21. Luzio, J. P. (2001). “Relationship between Endoscopes and Lysosomes”. Biochemical Society Transactions, Vol. 29, No.4, pp. 476-480, doi: 10.1042/bst0290476.
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