为不同的临床情景开发一个成本效益高且针对青光眼的OD/OC细分模型
Kai Liu1,2,3, Jicong Zhang1,2,4
1School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种新的青光眼细分模型,可以克服现有方法中的偏见. 它使用正常图像进行训练,有效地改进眼的进展跟踪和预后评估.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 现有的光盘 (OD) 和杯 (OC) 分段模型对正常样本有偏差,限制了它们在青光眼诊断和进展跟踪方面的有效性.
- 完全监督的模型需要昂贵的注释眼病数据,阻碍了广泛的临床应用.
研究的目的:
- 通过使用成本效益高的正常 fundus 图像,开发一款针对青光眼的专用细分模型.
- 调整模型以适应各种临床情景,并改善青光眼的进展监测和预后.
主要方法:
- 采用对比学习和域调整,利用来自正常样本的知识.
- 利用Gram矩阵用于风格信息和域编码的域调整.
- 应用了对比和对抗性学习,以尽量减少正常和青光眼样本之间的风格和域差异.
主要成果:
- 拟议的模型在两个公共数据集上在多个场景中表现出卓越的性能.
- 在细分和青光眼特异性指标方面都取得了高水平.
- 有效地缓解了现有的OD/OC细分模型中普遍存在的偏差问题.
结论:
- 开发的模型提供了一种注释效率高的战略,用于使用易于获得的正常 fundus 图像进行 DrDeramus 分析.
- 它解决了与完全监督的方法相关的经济和劳动负担.
- 在临床实践中显示出有助于对玻璃眼病进展的监测和预后评估的显著潜力.
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