Abstract
Automatic analysis of medical images is a challenging research which requires both the skill of a pathologist and computer vision knowledge to develop efficient systems. In this work, we have taken up the task of classifying different types of cell nuclei in histopathological Colon Cancer Images. We aim to show the relevance and effect of a complex histopathological dataset on the performance of current deep learning architectures. We have experimented with pre-trained (on ImageNet) AlexNet, VGG16, and VGG19 architectures and applied transfer learning approach to train these architectures. On the basis of the results obtained on the Histopathological image dataset, while using fine tuned AlexNet, VGG16, and VGG19 architectures; the suitability of using pure architectures is somehow questionable and these state of the art algorithms straightaway cannot be used for the sophisticated classification of very complex cancer tissue dataset. Comparative evaluation of the above state of the art methods have been done and the possibility of devising hybrid deep architectures is investigated thereof.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alex, K., Ilya, S., Geoffrey, H.: Image net classification with deep convolutional neural network. In: NIPS (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Santosh, K.C., Lamiroy, B., Wendling, L.: DTW-radon-based shape descriptor for pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 27(03), 1350008 (2013)
https://www.cancerresearchuk.org/what-is-cancer/how-cancer-starts/types-of-cancer
Zink, D., Fischer, A.H., Nickerson, J.A.: Nuclear structure in cancer cells. Nat. Rev. Cancer 4, 677–687 (2004). https://doi.org/10.1038/nrc1430
Santosh, K.C., Lamiroy, Bart, Wendling, Laurent: DTW for matching radon features: a pattern recognition and retrieval method. In: Blanc-Talon, Jacques, Kleihorst, Richard, Philips, Wilfried, Popescu, Dan, Scheunders, Paul (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 249–260. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23687-7_23. Inria-00617287
Liu, S., Mundra, P.A., Rajapakse, J.C.: Features for cells and nuclei classification. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, pp. 6601–6604 (2011). https://doi.org/10.1109/IEMBS.2011.6091628
Singh, M., Zeng, Z., Kalaw, E.M., Giron, D.M., Chong, K.-T., Lee, H.K.: A study of nuclei classification methods in histopathological images. In: Chen, Y.W., Tanaka, S., Howlett, R., Jain, L.C. (eds.) InMed 2017. SIST, vol. 71, pp. 78–88. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59397-5_9
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66–75 (2016). https://doi.org/10.1109/MIS.2016.24
Ravi, M., Hegadi, R.S.: Detection of Glomerulosclerosis in diabetic nephropathy using contour-based segmentation. In: International Conference on Advanced Computing Technologies and Applications ICACTA (2015)
Ukil, S., Ghosh, S., Obaidullah, S.M., Santosh, K.C., Roy, K., Das, N.: Deep learning for word-level handwritten Indic script identification, arXiv preprint arXiv:1801.01627
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27 (NIPS 2014). NIPS Foundation (2014)
Tripathi, S., Mishra, S., Singh, S.K.: Routine colon cancer detection using local image descriptors. In: IEEE Region 10 Conference (TENCON), Singapore 2016, pp. 2062–2065 (2016). https://doi.org/10.1109/TEN-CON.2016.7848388
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Process. Lett. 22(9), 1215–1219 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J. Biomed. Health Inform. 20(4), 1139–1147 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases. IEEE Trans. Image Process. 24(12), 5892–5903 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Rotation and scale invariant hybrid image descriptor and retrieval. Comput. Electr. Eng. 46, 288–302 (2015)
Dubey, S.R., Singh, S.K., Singh, R.K.: Local neighbourhood-based robust colour occurrence descriptor for colour image retrieval. IET Image Process. 9(7), 578–586 (2015)
Smith, S.L., Kindermans, P.J., Ying, C., Le, Q.V.: Don’t decay the learning rate, increase the batch size. In: ICLR 2018, arXiv preprint arXiv:1711.00489
Acknowledgment
This research was carried out in Indian Institute of Information Technology, Allahabad and supported by Ministry of Human Resource and Development, Government of India. We are also grateful to the NVIDIA corporation for supporting our research in this area. Currently, we are using a donated TITANX(PASCAL) GPU with 3584 CUDA cores to train models for this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tripathi, S., Singh, S. (2019). Histopathological Image Classification: Defying Deep Architectures on Complex Data. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_33
Download citation
DOI: https://doi.org/10.1007/978-981-13-9184-2_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9183-5
Online ISBN: 978-981-13-9184-2
eBook Packages: Computer ScienceComputer Science (R0)