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评估深度学习模型,以对具有有限数据和噪音标签的OCT图像进行分类.
Aleksandar Miladinović1, Alessandro Biscontin2, Miloš Ajčević3
1Institute for Maternal and Child Health IRCCS "Burlo Garofolo", Via dell'Istria 1, Trieste, 34100, Italy. aleksandar.miladinovic@burlo.trieste.it.
Scientific reports
|December 5, 2024
概括
深度学习模型从OCT图像中准确地分类视网膜疾病,但数据稀缺和标签噪声会降低性能. 增加培训数据大小可以减轻这些挑战,从而改善临床应用.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 深度学习 (DL) 对分类光学连贯断层扫描 (OCT) 图像来诊断视网膜疾病具有前途.
- 临床应用受到视网膜异常,图像噪声和人工制造物的变化所阻碍.
- 数据稀缺和标签噪声对训练强大的DL模型提出了重大挑战.
研究的目的:
- 评估各种DL架构 (ResNet18,ResNet34,ResNet50,VGG16,InceptionV3) 在海外图像分类方面的性能.
- 评估数据稀缺和标签噪声对视网膜病理检测中的DL模型准确性的影响.
- 确定最佳的训练数据大小和减轻错误标签错误的策略.
主要方法:
- 在5526张OCT图像的数据集上微调五个预训练DL架构.
- 在减少的子集上评估模型性能,减少到21张图像,以模拟数据稀缺.
- 评估10%,15%和20%标签噪声对分类准确性的影响.
主要成果:
- 所有DL架构都在345个或更多图像的训练集中实现了>90%的准确性.
- 在完整的数据集上,InceptionV3显示了最高的准确性 (99%).
- 减少样本大小和增加标签噪声显著降低了分类准确性和增加了变异性.
- 减轻10-20%的标签噪声需要4-14倍的图像来匹配345张正确标签图像的性能.
结论:
- DL模型,特别是InceptionV3,当在足够的数据 (≥345图像) 上训练时,可以准确地从OCT图像中分类视网膜病理.
- 数据稀缺性和标签噪声是影响海外国家和地区分析DL表现的关键因素.
- 增加培训数据集大小是克服错误标签错误的负面影响和提高诊断准确性的有效策略.
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