Mask Distillation Network for Conjunctival Hyperemia Severity Classification

Mingchao Li; Kun Huang; Xiao Ma; Yuexuan Wang; Wen Fan; Qiang Chen

doi:10.1007/s11633-022-1385-5

Volume 20 Issue 6

December 2023

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Mingchao Li, Kun Huang, Xiao Ma, Yuexuan Wang, Wen Fan, Qiang Chen. Mask Distillation Network for Conjunctival Hyperemia Severity Classification. Machine Intelligence Research, vol. 20, no. 6, pp.909-922, 2023. https://doi.org/10.1007/s11633-022-1385-5

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Mingchao Li, Kun Huang, Xiao Ma, Yuexuan Wang, Wen Fan, Qiang Chen. Mask Distillation Network for Conjunctival Hyperemia Severity Classification. Machine Intelligence Research, vol. 20, no. 6, pp.909-922, 2023. https://doi.org/10.1007/s11633-022-1385-5

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Mask Distillation Network for Conjunctival Hyperemia Severity Classification

doi: 10.1007/s11633-022-1385-5

Mingchao Li^{1

,},
Kun Huang^{1

,},
Xiao Ma^{1

,},
Yuexuan Wang^{1

,},
Wen Fan^{2

,},
Qiang Chen^{1

,}

1.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2.
Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China

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Author Bio:
Mingchao Li received the B. Sc. degree in engineering mechanics from School of Computer Science and Engineering, Nanjing University of Science and Technology, China in 2016. He is currently a Ph.D. degree candidate in control science and engineering at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interests include medical image processing and deep learning. E-mail: chaosli@njust.edu.cn ORCID iD: 0000-0001-7034-3357

Kun Huang received B. Sc. degree in computer science and technology from School of Computer Science and Technology, Zhejiang University of Technology, China in 2019. He is a Ph. D. degree candidate in control science and engineering at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interests include image generation and medical image processing. E-mail: 573172302@qq.com

Xiao Ma received M. Sc. degree in automation from School of Computer Science and Engineering, Nanjing University of Science and Technology, China in 2021. He is a Ph. D. degree candidate in computer science and technology at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interests include weakly supervised learning and medical image processing. E-mail: maxiao@njust.edu.cn

Yuexuan Wang received B. Sc. degree in electronic information science and technology from School of Physics and Information Technology, Shaanxi Normal University, China in 2017. She is a Ph. D. degree candidate in pattern recognition and intelligent system at School of Computer Science and Engineering from Nanjing University of Science and Technology, China. Her research interests include medical image processing and deep learning. E-mail: yxwang@njust.edu.cn

Wen Fan received M. D. degree in clinical medicine from Wuhan University, China in 2012. She is currently an associate chief physician and associate professor with First Affiliated Hospital of Nanjing Medical University, China. Her research interests include retinal imaging and vitreoretinal diseases. E-mail: fanwen1029@163.com

Qiang Chen received the B. Sc. degree in communication engineering and the Ph. D. degree in pattern recognition and intelligent system from Nanjing University of Science and Technology, China in 2002 and 2007, respectively. He held a post-doctoral position with Stanford University, USA from 2010 to 2011. He is currently a professor with Nanjing University of Science and Technology, China. His research interests include image processing and analysis, machine learning. E-mail: chen2qiang@njust.edu.cn (Corresponding author) ORCID iD: 0000-0002-6685-2447
Received Date: 2022-08-22
Accepted Date: 2022-10-20
Publish Date: 2023-12-01

Abstract

Abstract

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.
- Mask distillation (MD),
- conjunctiva hyperemia,
- attention mechanism,
- severity classification,
- deep learning

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References(39)

References

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Mask Distillation Network for Conjunctival Hyperemia Severity Classification

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