HEp-2 cell classification based on a Deep Autoencoding-Classification convolutional neural network

Jingxin Liu, Bolei Xu, LinLin Shen, Jon Garibaldi, Guoping Qiu

Abstract: In this paper, we present a novel deep learning model termed Deep Autoencoding-Classification Network (DACN) for HEp-2 cell classification. The DACN consists of an autoencoder and a normal classification convolutional neural network (CNN), while the two architectures shares the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error based on a multi-task learning procedure. We evaluate the proposed model using the publicly available ICPR2012 benchmark dataset. We show that this architecture is particularly effective when the training dataset is small which is often the case in medical imaging applications. We present experimental results to show that the proposed approach outperforms all known state of the art HEp-2 cell classification methods.