自主监督学习与对COVID-19医疗图像分类的自我蒸
Zhiyong Tan1, Yuhai Yu1, Jiana Meng1
1School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China.
Computer methods and programs in biomedicine
|October 24, 2023
概括
本研究介绍了自主监督学习与自主蒸的COVID-19医学图像分类 (SSSD-COVID) 来从有限的成像数据中诊断COVID-19. 该模型实现了高精度,帮助医生有效检测疾病.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- COVID-19诊断依赖于放射学,但深度学习模型需要大量的注释数据.
- 由于隐私和标签挑战,注释的COVID-19成像数据很少.
- 开发有效的深度学习模型,以有限的数据对COVID-19诊断至关重要.
研究的目的:
- 提出一个有效的深度学习模型用于COVID-19诊断,使用有限的注释医学图像.
- 帮助专业医生提高COVID-19检测的效率和准确性.
主要方法:
- 引入了面具自动编码器 (MAE),用于在小型数据集上进行预训练和微调.
- 关于COVID-19医学图像分类 (SSSD-COVID) 的自我蒸的拟议自主监督学习.
- 在潜在表示上实现了重建损失和自我蒸损失,以从解码器传输知识到编码器.
主要成果:
- 在SARS-COV-CT数据集 (2481张图像) 上达到97.78%的准确性,在COVID-CT数据集 (746张图像) 上达到81.76%.
- 整合外部知识提高了对数据集的准确率,达到99.6%和95.27%.
- 与其他模型相比,SSSD-COVID模型表现出优越的性能.
结论:
- 通过有限的数据,SSSSD-COVID有效地诊断COVID-19,优于现有的方法.
- 该模型可以通过引入外部信息来进一步增强,显著提高性能.
- 这种方法有助于医生做出决策,提高COVID-19检测效率.
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