Cross-Modal Transfer Learning for HEp-2 Cell Classification Based on Deep Residual Network

Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases. However, the traditional approach requires experienced experts to artificially identify cell patterns, which extremely increases the workload and suffer from the subjective opinion of physician. To address it, we propose a very deep residual network (ResNet) based framework to automatically recognize HEp-2 cell via cross-modal transfer learning strategy. We adopt a residual network of 50 layers (ResNet-50) that are substantially deep to acquire rich and discriminative feature. Compared with typical convolutional network, the main characteristic of residual network lie in the introduction of residual connection, which can solve the degradation problem effectively. Also, we use a cross-modal transfer learning strategy by pre-training the model from a very similar dataset (from ICPR2012 to ICPR2016-Task1). Our proposed framework achieves an average class accuracy of 95.63% on ICPR2012 HEp-2 dataset and a mean class accuracy of 96.87% on ICPR2016-Taskl HEp-2 dataset, which outperforms the traditional methods.