Mask Distillation Network for Conjunctival Hyperemia Severity Classification

To achieve automatic, fast and accurate severity classification of bulbar conjunctival hyperemia severity, we proposed a novel prior knowledge-based framework called mask distillation network (MDN). The proposed MDN consists of a segmentation network and a classification network with teacher-student branches. The segmentation network is used to generate a bulbar conjunctival mask and the classification network divides the severity of bulbar conjunctival hyperemia into four grades. In the classification network, we feed the original image and the image with the bulbar conjunctival mask into the student and teacher branches respectively, and an attention consistency loss and a classification consistency loss are used to keep a similar learning mode for these two branches. This design of “different input but same output”, named mask distillation (MD), aims to introduce the regional prior knowledge that “bulbar conjunctival hyperemia severity classification is only related to the bulbar conjunctiva region”. Extensive experiments on 5117 anterior segment images have proven the effectiveness of mask distillation technology: 1) The accuracy of the MDN student branch is 3.5% higher than that of a single optimal baseline network and 2% higher than that of the baseline network combination. 2) In the test phase, only the student branch is needed, and no additional segmentation network is required. The framework only takes 0.003 s to classify a single image, achieving the fastest speed in all the methods we compared. 3) Compared with a single baseline network, the attention of both teacher and student branches in the MDN has been intuitively improved.