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不同意见的整体蒸,以获得强大的病理图像分类

Kazuki Uehara, Wataru Uegami, Hirokazu Nosato

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

概括

准确的深度神经网络 (DNN) 需要一致的数据,但观察者的变性是一个挑战. 这项研究提出了一种新的DNN方法,使用多种模型来提高病理图像诊断的准确性,尽管专家的解释不同.

科学领域:

医疗图像分析医学图像分析
计算机视觉计算机视觉计算机视觉
机器学习机器学习

背景情况:

准确的深度神经网络 (DNN) 依赖于一致的标记数据.
在标签上观察者间的变化,特别是在病理图像诊断中,是一个重大挑战.
关于构建具有模两可的基本真相的DNNs的现有研究是有限的.

研究的目的:

为了应对病理图像诊断中观察者间变异性的挑战.
开发一种方法来构建强大的DNN模型,尽管有不一致的专家标签.
在存在标签模两可的情况下,提高DNN的概括能力和分类准确性.

主要方法:

提出了一种构建DNN模型的新方法,该方法利用来自多个DNN的知识.
利用各种DNN模型学习的数据之间的关系来提高稳定性.
使用多种病理学数据集进行比较实验,并由不同病理学家独立标记.

主要成果:

拟议的方法在不同数据集中表现出良好的概括能力.
与基线模型相比,实现了更高的分类准确性.
有效地减轻了观察者之间的变异性对DNN性能的影响.

结论:

开发的方法提供了一种强大的方法来构建DNN用于病理图像诊断,具有观察者间的可变性.
这种技术提高了人工智能驱动的诊断工具的可靠性和准确性.
解决了将DNN应用于主观医学图像解释的关键差距.

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