用深度学习最先进的架构来分类口腔上皮质质变形:一个比较的评估评估
Anna Luíza Damaceno Araújo1,2, Viviane Mariano da Silva3, Matheus Cardoso Moraes3
1Head and Neck Surgery Department, University of São Paulo Medical School, São Paulo, Brazil.
概括
机器学习模型对口腔上皮质质变形表现出不良的概括性,表明它们不能在现实临床环境中可靠地区分高风险和低风险的病变. 需要进一步的研究来提高这些潜在的恶性疾病的诊断准确性.
科学领域:
- 口腔病理学 口腔病理学
- 数字病理学数字病理学
- 医学中的人工智能
背景情况:
- 在病理学家之间口腔上皮质质变形症分级的差异需要客观的诊断工具.
- 机器学习 (ML) 提供了一个潜在的解决方案,以标准化失生症评估.
研究的目的:
- 为了评估最先进的卷积神经网络 (CNN) 在分类口腔上皮质发育不良时的性能.
- 评估ML模型对口腔潜在恶性疾病的概括能力.
主要方法:
- 使用了来自82名患有口腔潜在恶性疾病的患者的98张整片图像的数据集.
- 图像被注释为发育不良,细分为补丁,和增强.
- 七个CNN在81,786个补丁上接受了培训和验证,并在4,486个补丁上进行了测试.
主要成果:
- 模型表现出高的学习率,但有限的概括潜力.
- VGG16在测试组件中表现最好 (精度62%,AUC65%),但受到过度装配的影响.
- 效率B0显示了具有最低损失的可比度量,表明未来发展的前景.
结论:
- 目前的ML模型很难将其推广到现实世界的数据中,因为低风险和高风险发育不良之间的特征重叠.
- 开发的CNN还不适合临床应用,用于区分口腔上皮质质变形等级.
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