强大与弱弱数据标签用于人工智能算法在测量地理缩的测量
Amitha Domalpally1,2, Robert Slater1, Rachel E Linderman1,2
1A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin.
Ophthalmology science
|June 3, 2024
概括
深度学习模型使用基底自光 (FAF) 图像准确地测量地理缩 (GA). 结合弱标和强标数据,为训练眼科人工智能提供了有效的解决方案.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 地理缩 (GA) 测量对于监测与年龄相关的黄斑变性至关重要.
- 准确的GA量化需要详细的图像分析,通常涉及手动细分.
- 深度学习模型显示出从基底自光 (FAF) 图像自动化GA测量的承诺.
研究的目的:
- 评估数据标签要求,以训练使用FAF图像进行GA测量的深度学习模型.
- 为了比较训练有素的AI模型与不同级别的数据标签的性能.
主要方法:
- 使用了两组FAF图像:弱标记 (仅区域测量) 和强标记 (GA细分面具).
- 与年龄相关的眼病研究2 (AREDS2) 数据集用于培训和交叉验证.
- 临床试验图像用于测试AI模型.
主要成果:
- 深度学习模型实现了与人类分级器可比的GA面积测量,即使数据标签很弱.
- 子系数在细分方面表现出高精度,交叉验证值为0.89,测试值为0.92.
- 整合大量弱标签图像与少量强标签图像的集成证明是有效的.
结论:
- 深度学习模型可以使用弱标记的FAF图像在GA测量中实现合理的准确性.
- 混合训练方法结合弱标和强标数据是眼科人工智能开发的成本效益高的解决方案.
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