A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning

Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases and subsequent treatment. One of the key challenges is huge intra-class variations caused by inhomogeneous illumination. To address it, we propose a framework based on very deep supervised residual network (DSRN) to classify HEp-2 cell images. Specifically, we adopt a residual network of 50 layers (ResNet-50) that is substantially deep to extract rich and discriminative features. The deep supervision is imposed on the ResNet-based framework to further boost the classification performance by directly guiding the training of the lower and upper levels of the network. The proposed method is evaluated using two publicly available datasets (i.e., International Conference on Pattern Recognition (ICPR) 2012 and ICPR2016-Task1 cell classification contest datasets). Different from the previous deep learning models learned from scratch, a cross-modal transfer learning strategy is developed. Namely, we pretrain ICPR2012 dataset to fine-tune ICPR2016 dataset based on our DSRN model since both datasets are similar. Extensive experiments show that the proposed method delivers state-of-the-art performance and outperforms the traditional methods based on deep convolutional neural network (DCNN).