Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images
文献类型：期刊论文
| 作者 | Li, Chun-Peng1,2; Dai, Weiwei3; Xiao, Yun-Peng1; Qi, Mengying4; Zhang, Ling-Xiao1; Gao, Lin1,2; Zhang, Fang-Lue7; Lai, Yu-Kun8; Liu, Chang9; Lu, Jing10 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2024-08-08 |
| 卷号 | 14期号:1页码:11 |
| 关键词 | Fungal keratitis Image classification Neural network Transformer |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-024-68768-y |
| 英文摘要 | Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis. |
| 资助项目 | Science and Technology Service Network Initiative of the Chinese Academy of Sciences[SZYK202204] ; Aier-ICT Joint Laboratory for Digital Ophthalmology |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001294094100046 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39619]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zeng, Qingyan; Chen, Yiqiang |
| 作者单位 | 1.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Changsha Aier Eye Hosp, Changsha, Hunan, Peoples R China 4.Wuhan Aier Hankou Eye Hosp, Wuhan, Peoples R China 5.Wuhan Univ, Aier Eye Hosp, Wuhan, Peoples R China 6.Hubei Univ Sci & Technol, Xianning, Peoples R China 7.Victoria Univ Wellington, Wellington, New Zealand 8.Cardiff Univ, Cardiff, Wales 9.Beijing Aier Intech Eye Hosp, Beijing, Peoples R China 10.Chengdu Aier East Eye Hosp, Chengdu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chun-Peng,Dai, Weiwei,Xiao, Yun-Peng,et al. Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images[J]. SCIENTIFIC REPORTS,2024,14(1):11. |
| APA | Li, Chun-Peng.,Dai, Weiwei.,Xiao, Yun-Peng.,Qi, Mengying.,Zhang, Ling-Xiao.,...&Chen, Yiqiang.(2024).Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images.SCIENTIFIC REPORTS,14(1),11. |
| MLA | Li, Chun-Peng,et al."Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images".SCIENTIFIC REPORTS 14.1(2024):11. |
入库方式： OAI收割
来源：计算技术研究所
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