返回
Foundation models for ophthalmic imaging
DOI:10.1016/j.survophthal.2026.01.004.png)
摘要
En 中文
Foundation models represent a new frontier in ophthalmic artificial intelligence, enabling learning of transferable features from large unlabelled imaging datasets for flexible application to varying downstream tasks.
期刊
IF:
5.9
论文数:
2.4K
被引数:
7.8K
机构
引用论文
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging用于诊断成像的自监督机器学习的鲁棒且数据高效的泛化
Training a high-performance retinal foundation model with half-the-data and 400 times less compute使用一半数据和计算量减少400倍训练高性能视网膜基础模型
Nature Communications
IF15.7
Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model利用知识丰富的视觉-语言模型提升罕见和常见眼底疾病的诊断准确性
Foundation model-driven distributed learning for enhanced retinal age prediction基础模型驱动的分布式学习以增强视网膜年龄预测
Development and Validation of a Multimodal Multitask Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
NEJM AI
IF0

