标注 有效学习用于OCT细分
Haoran Zhang1, Jianlong Yang1, Ce Zheng2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Biomedical optics express
|July 27, 2023
概括
本研究引入了一种高效的深度学习方法,用于光学一致性断层扫描 (OCT) 细分,显著减少数据注释需求. 该方法以更少的数据和更快的培训时间实现了可比的准确性,增强了OCT技术应用.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 对于OCT细分的深度学习模型需要大量的数据注释.
- 标注是耗时和昂贵的,限制了OCT在诸如外科导航和多中心试验等领域的应用.
研究的目的:
- 开发一个注释效率高的学习方法,用于OCT细分.
- 为了减少与OCT细分模型数据注释相关的成本和时间.
主要方法:
- 利用基于变压器的模型用于OCT图像的自我监督生成学习.
- 将变压器编码器与CNN解码器集成在一起,用于密集的像素智能预测.
- 引入了用于选择性数据注释的k中心算法.
主要成果:
- 仅使用一小部分培训数据,实现了与U-Net相似的细分精度.
- 演示了高达3.5倍更快的训练时间.
- 优于其他注释效率策略.
结论:
- 拟议的方法大大降低了标注费用和用于OCT细分的培训时间.
- 预训练模型适应各种数据集和ROI,无需重新训练.
- 这种学习效率可以提高OCT技术的智能化和采用.
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