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Mask Distillation Network for conjunctival hyperemia Severity Classification

Machine Intelligence Research 2023年第6期20卷 909-922页

作者： Mingchao Li Kun Huang Xiao Ma Yuexuan Wang Wen Fan Qiang Chen School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Department of Ophthalmology The First Affiliated Hospital with Nanjing Medical UniversityNanjing210029China

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 *** segmentation network is used to generate a bulbar conjunctival mask and the classification network divides the severity of bulbar conjunctival hyperemia into four *** 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 *** 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 *** 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.

关键词： Mask distillation(MD) conjunctiva hyperemia attention mechanism severity classification deep learning

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