Automated cell classification in Indirect Immunofluorescence (IIF) images has potential to be an important tool in clinical practice and research. Recently, classification of Human Epithelial Type 2 (HEp-2) cell images has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intra-class and small inter-class variations. We have proposed several effective approaches to this problem, using texture and shape descriptors as well as convolutional neural networks.
Qi X, Zhao G, Chen J & Pietikäinen M (2016) HEp-2 cell classification: The role of Gaussian scale space theory as a pre-processing approach. Pattern Recognition Letters, in press (available online).
Qi X, Zhao G, Li C-G, Guo J & Pietikäinen M (2016) HEp-2 cell classification via combining multi-resolution co-occurrence texture and large regional shape information. IEEE Journal of Biomedical and Health Informatics, in press (available online).
Bayramoglu N, Kannala J & Heikkilä J (2015) Human epithelial type 2 cell classification with convolutional neural networks. Proc. IEEE International Conference on Bioinformatics & Bioengineering (BIBE), Belgrade, Serbia, 1-6. Project Page
Qi X, Zhao H, Chen J & Pietikäinen M (2016) Exploring illumination robust descriptors for human epithelial type 2 cell classification. Pattern Recognition, accepted
Last updated: 26.8.2016