@inproceedings{790e9f24329f4831b10414a7bd7f4a71,
title = "Deep convolutional neural network based HEp-2 cell classification",
abstract = "As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26% on ICPR 2016 HEp-2 training set.",
keywords = "CNN, Class-balanced, Classification, Hep-2",
author = "Xi Jia and Linlin Shen and Xiande Zhou and Shiqi Yu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1109/ICPR.2016.7899611",
language = "English",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "77--80",
booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR 2016",
address = "United States",
}
