  Automatic classification of Human Epithelial Type-2 (HEp-2) cells staining
patterns is an important and yet a challenging problem. Although both shallow
and deep methods have been applied, the study of deep convolutional networks
(CNNs) on this topic is shallow to date, thus failed to claim its top position
for this problem. In this paper, we propose a novel study of using CNNs for
HEp-2 cells classification focusing on cross-specimen analysis, a key
evaluation for generalization. For the first time, our study reveals several
key factors of using CNNs for HEp-2 cells classification. Our proposed system
achieves state-of-the-art classification accuracy on public benchmark dataset.
Comparative experiments on different training data reveals that adding
different specimens,rather than increasing in numbers by affine
transformations, helps to train a good deep model. This opens a new avenue for
adopting deep CNNs to HEp-2 cells classification.
