共同伪标签和积极选择的基金单积极多标签学习
IEEE transactions on medical imaging
|April 28, 2025
概括
这项研究引入了一种新的方法,用于训练 fundus 图像的深度学习模型,通过解决不完整的注释来改进多标签疾病分类. FSP方法提高了视网膜疾病的诊断准确性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 使用 fundus 图像诊断视网膜疾病往往遭受不完整的多标签注释.
- 不准确或不完整的训练数据可能导致深度学习模型性能差以及错误诊断.
研究的目的:
- 开发一种有效的方法,从带有单个正注释的基底图像中进行多标签学习.
- 提高视网膜疾病深度学习分类器的准确性和可靠性.
主要方法:
- 提出了一种名为FSP (Fundus单积极多标签学习) 的新方法.
- FSP采用两个网络的同时培训,课程配合伪标签和活跃的样本选择.
- 课程共伪标签动态调整值,而主动样本选择使用损失建模来改进伪标签.
主要成果:
- 对七个视网膜数据集的实验评估表明了FSP方法的有效性.
- 在多标签基金图像分类中,FSP显示优于现有方法.
- 下游实验验证实了拟议方法的实际实用性.
结论:
- FSP方法有效地解决了在基金图像中不完整的多标签注释的挑战.
- 这种方法提高了用于视网膜疾病诊断的深度学习模型的准确性.
- FSP为改善眼科诊断结果提供了一个有希望的解决方案.
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