@inproceedings{8f2c4ba3e3fd4fe4a806fbf6c96ab5d8,
title = "Multi-Modality Semi-Supervised Learning for Ophthalmic Biomarkers Detection",
abstract = "Ophthalmic Biomarkers, as an objective and quantifiable approach to identifying the ophthalmological disease process, are proven to be useful not only in assisting healthcare professionals in disease diagnosis but also in the identification of phenomena and risk factors in the early stages, which greatly contribute to disease prevention and better treatment of patients. In this study, a deep learning method is introduced to achieve simultaneous automatic recognition of six prevalent ophthalmic biomarkers in the OLIVES dataset. To enhance identification accuracy, semi-supervised learning techniques are adopted in this research and different data modalities are jointly optimized using a guided loss function. The experimental results reveal that the method reaches an F1 score of 0.70 on a test set with 3,872 images.",
keywords = "disease diagnosis, multi-modality, ophthalmic biomarkers, semi-supervised learning",
author = "Yanming Chen and Chenxi Niu and Chen Ye and Shengji Jin and Yue Li and Chi Xu and Keyi Liu and Haowei Gao and Jingxi Hu and Yuanhao Zou and Huizhong Zheng 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 = "10.1117/12.3019655",
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",
}