MBT:基于模型的变压器用于视网膜光学连贯性断层扫描图像和视频多重分类.
Badr Ait Hammou1, Fares Antaki2, Marie-Carole Boucher1
1Department of Ophthalmology, Université de Montréal, Montreal, Québec, Canada; Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de l'Est-de-l'Île-de-Montréal, Montréal, Québec, Canada.
International journal of medical informatics
|September 1, 2023
概括
这项研究引入了一种新的基于模型的变压器 (MBT) 来从光学连贯断层扫描 (OCT) 数据中检测视网膜疾病. MBT增强了变压器模型的性能,优于现有的OCT图像和视频分类方法,以提高诊断准确度.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 视网膜疾病的检测是医学成像中的关键分类问题.
- 光学连贯断层扫描 (OCT) 是可视化视网膜结构的关键技术.
- 变压器模型有希望,但需要提高临床应用的性能.
研究的目的:
- 开发一种有效的技术,使用OCT数据自动检测视网膜疾病.
- 改进现有变压器模型的性能,用于海外国家和地区的图像和视频分类.
- 引入一种新的方法,用于增强特征表示和海外国家和地区数据的分类.
主要方法:
- 提出了一个基于模型的变压器 (MBT) 技术,利用预先训练的视觉变压器,旋转变压器和多尺度视觉变压器模型.
- 用于OCT数据特征提取和最佳特征估计的近似稀疏表示.
- 将MBT方法应用于OCT图像和OCT视频分类任务.
主要成果:
- 在现实世界的视网膜数据集上,MBT方法显著超过了最先进的深度学习方法.
- 取得了卓越的分类准确性,精度,回忆,F1分数,卡帕,AUC-ROC和AUC-PR.
- 在OCT分析中,视觉变压器,Swin变压器和多尺度视觉变压器的性能得到了改善.
结论:
- 这种MBT方法提供了一种自动检测视网膜疾病的新方法,可以从OCT数据中检测出视网膜疾病.
- 这项研究强调了使用OCT视频的潜力,而不仅仅是图像,用于识别视网膜病理.
- 这些发现为加强眼科研究和临床实践中的深度学习模型提供了有价值的框架.
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