用于眼科成像的基础模型
Kayvan Gharbi1, Peter van Wijngaarden2, Xavier Hadoux1
1Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia.
Survey of ophthalmology
|January 31, 2026
概括
眼科基础模型正在从专业化发展为多功能AI工具. 最近的进展侧重于整合各种数据和文本指导,以改善眼科护理应用程序.
科学领域:
- 眼科人工智能 眼科人工智能
- 医疗保健中的机器学习
- 医学成像分析 医学成像分析
背景情况:
- 基础模型可以从大型,未标记的眼科数据集中提供可转移的特征学习.
- 这些模型对于推动眼科人工智能的发展至关重要.
- 它们可以灵活地应用于各种下游诊断和分析任务.
研究的目的:
- 从2022年到2025年7月,系统地分析眼科基础模型的演变.
- 检查模型架构,预培训和监督策略的关键进展.
- 提供当前趋势和该领域未来方向的全面概述.
主要方法:
- 对12种不同的眼科基础模型进行系统审查.
- 分析模式整合 (单模式,多模式,视觉语言).
- 评估预训练目标 (生成与对比) 和监督 (图像/文本导向).
主要成果:
- 观察到从特定领域的单模模式转向以临床文本为指导的模态不可知模型.
- 新兴技术,如模态不可知编码器和合成数据增强,提高了性能和通用性.
- 预培训和监督策略的进步是进步的关键驱动力.
结论:
- 眼科基础模型越来越复杂,朝着更广泛的适用性迈进.
- 未来的研究应该专注于更广泛的模式集成,更高维度的数据和标准化的基准.
- 本综述为开发和应用这些人工智能模型在眼科领域提供了基础知识.
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