Towards Automated Multiscale Imaging and Analysis in TEM: Glomerulus Detection by Fusion of CNN and LBP Maps
Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.
KeywordsGlomerulus detection Transmission Electron Microscopy Convolutional Neural Networks Local binary patterns Digital pathology
This work is supported by VINNOVA, MedTech4Health grants 2016-02329 and 2017-02447, the Ministry of Education, Science, and Techn. Development of the Rep. of Serbia (proj. ON174008 and III44006), and the Centre for Interdisciplinary Mathematics, Uppsala University.
- 3.Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)Google Scholar
- 5.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
- 6.Juefei-Xu, F., Boddeti, V.N., Savvides, M.: Local binary convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 19–28. IEEE (2017)Google Scholar
- 8.Levi, G., Hassner, T.: Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In: Proceedings of ACM International Conference on Multimodal Interaction, pp. 503–510. ACM (2015)Google Scholar
- 12.Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y.: Combining deep learning and hand-crafted features for skin lesion classification. In: International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 1–6. IEEE (2016)Google Scholar
- 19.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)