Deeply Supervised Residual Network for HEp-2 Cell Classification

Abstract

To diagnose various autoimmune diseases, the accurate Human Epithelial-2 (HEp-2) cell image classification is a very important step. Automatic classification of HEp-2 cell using microscope image is a highly challenging task due to the strong illumination changes derived from the low contrast of the cells. To address this challenge, we propose a deep residual network (ResNet) based framework to recognize HEp-2 cell automatically. Specifically, a residual network of 50 layers (ResNet-50) with substantial deep layer is adopted to acquire the informative feature for accurate recognition. To further boost the recognition performance, we devise a novel ResNet-based network with deep supervision. The deeply supervised ResNet (DSRN) can address the optimization problem of gradient vanishing/exploding and accelerate the convergence speed. DSRN can directly guide the training of the lower and upper levels of the network to counteract the effects of unstable gradient variations by the adverse training process. As a result, DSRN can extract more discriminative features. Experimental results show that our proposed DSRN method can achieve an average classification accuracy of 93.46% and 95.88% on ICPR20l2 and ICPR20l6- Taskl datasets, respectively. Our proposed method outperforms the traditional methods as well.