@inproceedings{5459024da2b84574824f7d5d675bda0e,
title = "Deep Learning for Retina Structural Biomarker Classification Using OCT Images",
abstract = "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.",
keywords = "Biomarker analysis, Deep Learning, Multi-modal Learning, Multi-task Learning, Retinal OCT",
author = "Chi Xu and Huizhong Zheng and Keyi Liu and Yanming Chen and Chen Ye and Chenxi Niu and Shengji Jin and Yue Li and Haowei Gao and Jingxi Hu and Yuanhao Zou and Xiangjian He",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 International Workshop on Advanced Imaging Technology, IWAIT 2024 ; Conference date: 07-01-2024 Through 08-01-2024",
year = "2024",
doi = "10.1117/12.3026739",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Lau, {Phooi Yee} and Jae-Gon Kim and Hiroyuki Kubo and Chuan-Yu Chang and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2024",
address = "United States",
}