Gated Channel Attention Network for Cataract Classification on AS-OCT Image

Zunjie Xiao; Xiaoqing Zhang; Risa Higashita; Yan Hu; Jin Yuan; Wan Chen; Jiang Liu

doi:10.1007/978-3-030-92238-2_30

Gated Channel Attention Network for Cataract Classification on AS-OCT Image

Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Yan Hu, Jin Yuan, Wan Chen, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.

Original language	English
Title of host publication	Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
Editors	Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	357-368
Number of pages	12
ISBN (Print)	9783030922375
DOIs	https://doi.org/10.1007/978-3-030-92238-2_30
Publication status	Published - 2021
Externally published	Yes
Event	28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online Duration: 8 Dec 2021 → 12 Dec 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13110 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Neural Information Processing, ICONIP 2021
City	Virtual, Online
Period	8/12/21 → 12/12/21

Keywords

AS-OCT
Deep learning
Gated channel attention
Nuclear cataract

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-92238-2_30

Cite this

Xiao, Z., Zhang, X., Higashita, R., Hu, Y., Yuan, J., Chen, W., & Liu, J. (2021). Gated Channel Attention Network for Cataract Classification on AS-OCT Image. In T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings (pp. 357-368). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13110 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92238-2_30

Xiao, Zunjie ; Zhang, Xiaoqing ; Higashita, Risa et al. / Gated Channel Attention Network for Cataract Classification on AS-OCT Image. Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. editor / Teddy Mantoro ; Minho Lee ; Media Anugerah Ayu ; Kok Wai Wong ; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. pp. 357-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Gated Channel Attention Network for Cataract Classification on AS-OCT Image",

abstract = "Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.",

keywords = "AS-OCT, Deep learning, Gated channel attention, Nuclear cataract",

author = "Zunjie Xiao and Xiaoqing Zhang and Risa Higashita and Yan Hu and Jin Yuan and Wan Chen and Jiang Liu",

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Xiao, Z, Zhang, X, Higashita, R, Hu, Y, Yuan, J, Chen, W & Liu, J 2021, Gated Channel Attention Network for Cataract Classification on AS-OCT Image. in T Mantoro, M Lee, MA Ayu, KW Wong & AN Hidayanto (eds), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13110 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 357-368, 28th International Conference on Neural Information Processing, ICONIP 2021, Virtual, Online, 8/12/21. https://doi.org/10.1007/978-3-030-92238-2_30

Gated Channel Attention Network for Cataract Classification on AS-OCT Image. / Xiao, Zunjie; Zhang, Xiaoqing; Higashita, Risa et al.
Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. ed. / Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. p. 357-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13110 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Xiao, Zunjie

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AU - Yuan, Jin

AU - Chen, Wan

AU - Liu, Jiang

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N2 - Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.

AB - Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.

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Xiao Z, Zhang X, Higashita R, Hu Y, Yuan J, Chen W et al. Gated Channel Attention Network for Cataract Classification on AS-OCT Image. In Mantoro T, Lee M, Ayu MA, Wong KW, Hidayanto AN, editors, Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 357-368. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-92238-2_30