自主监督学习用于对上鼻异常的分类,在上鼻.
Debayan Bhattacharya1,2, Finn Behrendt3, Benjamin Tobias Becker4
1Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany. debayan.bhattacharya@tuhh.de.
概括
这项研究引入了一种新的自我监督学习方法,用于检测上鼻中上鼻的异常. 该方法有效地识别出异常,即使有有限的标记数据,也优于现有方法.
科学领域:
- 医学成像分析 医学成像分析
- 放射学中的人工智能
- 计算病理学计算病理学
背景情况:
- 上鼻异常是放射检查中常见的发现,呈现出不同的形态.
- 对异常分类的监督学习需要广泛的标记数据集,这些数据集通常是不可用的.
- 自主监督学习 (SSL) 为从未标记的数据中学习提供了一个有希望的替代方案.
研究的目的:
- 开发一种专门用于对上鼻 (MS) 中上鼻异常进行分类的SSL方法.
- 为了应对有限的标记数据在训练准确的异常检测模型中的挑战.
- 提高识别各种鼻异常形态的效率和有效性.
主要方法:
- 在无监督异常检测 (UAD) 框架内使用了3D卷积自编码器 (CAE).
- 训练CAE重建正常的MS图像,生成突出异常的残余图像.
- 在剩余图像上使用3D卷积神经网络 (CNN) 执行SSL任务,然后对标记数据进行微调.
主要成果:
- 拟议的SSL技术比通用SSL方法表现出更高的性能,特别是在有限的注释数据下.
- 在MS异常分类的注释数据集的10%上,获得了0.79的精度回忆曲线 (AUPRC) 下的面积.
- 在AUPRC中表现优于BYOL (0.75),SimSiam (0.74),SimCLR (0.73) 和基于Spark的掩盖自动编码 (0.75).
结论:
- 专注于局部化鼻异常的SSL方法有利于区分正常与异常的上鼻.
- 该方法显示了在缺乏标记医疗成像数据的场景中提高诊断准确性的巨大潜力.
- 代码可在https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly下找到,以便进一步研究和应用.
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