语义对比与不确定性意识伪标签的腰部半监督分类的语义对比
Jinjin Hai1, Jian Chen1, Kai Qiao1
1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, China.
Computers in biology and medicine
|June 15, 2024
概括
SeCoFixMatch通过使用磁共振成像 (MRI) 增强了腰椎间盘 (LDH) 诊断的半监督学习. 这种方法减少了80%的注释需求,超过了用标签数据显著减少的传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 腰椎间盘 (LDH) 诊断依赖于MRI,但手动注释是劳动密集的.
- 半监督学习 (SSL) 使用有限的标记和丰富的未标记数据进行深度学习.
- 现有的SSL方法,如伪标签和一致性规范化,具有局限性.
研究的目的:
- 引入SeCoFixMatch,这是一个SSL方法,可以通过MRI高效地进行LDH诊断.
- 解决当前SSL技术的局限性,特别是伪标签中的确认偏差和一致性规范化方面的指导缺乏.
- 提高基于深度学习的LDH诊断的准确性和减少注释力度.
主要方法:
- SeCoFixMatch将语义对比和不确定性意识的伪标签集成到SSL中.
- 语义对比确保了标记和未标记的MRI图像之间的语义一致性.
- 不确定性意识的伪标签结合了预测信心和不确定性,通过KL损失与狄里克莱特分布计算.
主要成果:
- 与其他SSL模型相比,SeCoFixMatch表现出卓越的有效性和通用性.
- 废弃研究证实了模型在不同数量的标记数据中的性能.
- 只有40个标记的图像,SeCoFixMatch超过了在200个标签上训练的基线模型,减少了80%的注释工作.
结论:
- 拟议的伪标签算法产生精确的标签,增强语义对比学习.
- 语义对比学习改善了特征表示,提高了伪标签预测的准确性.
- 伪标签和语义对比之间的协同相互作用显著提高了SSL在LDH诊断中的性能.
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