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动量对比变压器用于COVID-19诊断与知识蒸

Aimei Dong¹, Jian Liu¹, Guodong Zhang¹

¹Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Pattern recognition

|June 12, 2023

概括

一种新方法,即动量对比学习和知识蒸 (MCT-KD),可以从CT扫描中改进COVID-19的诊断. 它有效地分析全球和本地图像特征,提高了这种呼吸系统疾病的识别精度.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机辅助诊断计算机辅助诊断

背景情况:

对新型冠状病毒疾病 (COVID-19) 的智能诊断至关重要.
现有的深度学习模型在COVID-19胸部CT图像中难以利用全球和本地特征,从而限制了诊断准确度.
在小型数据集上训练像Vision Transformer这样的深度模型存在挑战.

研究的目的:

提出一种新的方法,即动量对比学习和知识蒸 (MCT-KD),用于使用胸部CT图像改进COVID-19诊断.
加强从CT图像中提取全球和本地特征,以更准确地检测COVID-19.
解决在有限的数据集上训练深度学习模型的局限性.

主要方法:

使用视觉转换器,具有势头对比的学习任务,用于有效的全局特征提取.
在传输和微调过程中通过知识蒸将集成卷积局部集成到视觉变压器中.
雇员自主监督的势头对比学习以减轻小数据集的挑战.

主要成果:

拟议的MCT-KD方法有效地从COVID-19胸部CT图像中提取全球和本地特征.
在两个独立的公共数据集上实现了高诊断准确度,达到87.43%和96.94%.
证明了将势头对比学习和知识蒸用于COVID-19检测的有效性.

结论:

MCT-KD方法为智能COVID-19诊断提供了一个强大的方法.
全球和本地特征分析的整合显著提高了识别准确性.
通过势头对比学习进行自我监督学习,对有限的医学成像数据集的培训是有益的.

关键词:

诊断COVID-19的诊断结果是什么知识的蒸知识的蒸.势头对比的学习学习.视觉变压器视觉变压器

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