Deep Learning for Retina Structural Biomarker Classification Using OCT Images

被引：0

作者：

Xu, Chi ^{[1
]}

Zheng, Huizhong ^{[1
]}

Liu, Keyi ^{[1
]}

Chen, Yanming ^{[1
]}

Ye, Chen ^{[1
]}

Niu, Chenxi ^{[1
]}

Jin, Shengji ^{[1
]}

Li, Yue ^{[1
]}

Gao, Haowei ^{[1
]}

Hu, Jingxi ^{[1
]}

Zou, Yuanhao ^{[1
]}

He, Xiangjian ^{[1
]}

机构：

[1] Univ Nottingham, Ningbo, Peoples R China

来源：

INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY, IWAIT 2024 | 2024年 / 13164卷

关键词：

Deep Learning; Retinal OCT; Multi-task Learning; Multi-modal Learning; Biomarker analysis;

D O I：

10.1117/12.3026739

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study presents an approach to identifying retinal structural biomarkers in ophthalmology, which is essential for accurate diagnosis and effective treatment of eye diseases. We develop a multi-modal, multi-task deep learning framework that integrates supervised and semi-supervised training methods. This model effectively processes a combination of 3D Optical Coherence Tomography (OCT) images and one-dimensional clinical data. A key advancement is introducing a custom post-processing method that significantly improves the precision of biomarker detection. Our model successfully identifies six distinct biomarkers in the retina and achieves a notable macro f1-score of 71.62%, representing a substantial 14.48% improvement over the baseline performance. This advancement underscores the potential of deep learning in enhancing diagnostic accuracy and treatment efficacy in ophthalmology.

引用

页数：6

共 19 条

[1]

AlRegib G., 2023 ieee sps video and image processing (vip) cup

[2]

Bandello F, 2019, Clinical strategies in the management of diabetic retinopathy: a step-by-step guide for ophthalmologists Internet, P97, DOI [10.1007/978- 3-319-96157-6_3, DOI 10.1007/978-3-319-96157-6_3]

[3] A review of the application of deep learning in medical image classification and segmentation [J].

Cai, Lei ;

Gao, Jingyang ;

Zhao, Di .

ANNALS OF TRANSLATIONAL MEDICINE, 2020, 8 (11)

[4]

Cheung CYL, 2021, INVEST OPHTH VIS SCI, V62

[5] Deep learning approach for diabetic retinopathy screening [J].

Colas, E. ;

Besse, A. ;

Orgogozo, A. ;

Schmauch, B. ;

Meric, N. ;

Besse, E. .

ACTA OPHTHALMOLOGICA, 2016, 94

[6]

Crawshaw M., 2020, arXiv, DOI [10.48550/arXiv.2009.09796, DOI 10.48550/ARXIV.2009.09796]

[7] A Survey on Deep Learning for Multimodal Data Fusion [J].

Gao, Jing ;

Li, Peng ;

Chen, Zhikui ;

Zhang, Jianing .

NEURAL COMPUTATION, 2020, 32 (05) :829-864

[8] Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images [J].

Garcia, Gabriel ;

Gallardo, Jhair ;

Mauricio, Antoni ;

Lopez, Jorge ;

Del Carpio, Christian .

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II, 2017, 10614 :635-642

[9] Deep Residual Learning for Image Recognition [J].

He, Kaiming ;

Zhang, Xiangyu ;

Ren, Shaoqing ;

Sun, Jian .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778

[10] A Survey on Contrastive Self-Supervised Learning [J].

Jaiswal, Ashish ;

Babu, Ashwin Ramesh ;

Zadeh, Mohammad Zaki ;

Banerjee, Debapriya ;

Makedon, Fillia .

TECHNOLOGIES, 2021, 9 (01)

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