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基于深度学习的光盘分类受光盘倾斜的影响.

Youngwoo Nam1,2, Joonhyoung Kim3, Kyunga Kim2,4,5

  • 1Medical AI Research Center, Institute of Smart Healthcare, Samsung Medical Center, Seoul, Republic of Korea.

Scientific reports
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

光盘倾斜显著影响深度学习对光盘外观的分类准确性. 在非倾斜磁盘上训练的模型表现更好,突出了需要在AI开发中考虑倾斜的必要性.

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 深度学习模型越来越多地用于 fundus 图像中的光盘分析.
  • 光盘倾斜是一种常见的解剖变异,可能会影响分类性能.

研究的目的:

  • 评估光盘倾斜对基于深度学习的光盘分类准确性的影响.
  • 为了比较在倾斜与非倾斜光盘数据集上训练的深度学习模型的性能.

主要方法:

  • 利用了来自1809名受试者的2507张 fundus照片的数据集,其中40.3%的受试者表现出倾斜的光盘.
  • 对正常和病态光盘变化的注释图像 (眼球瘤,胀,白).
  • 开发和比较深度学习分类模型 (VGG16,VGG19,DenseNet121) 使用所有学科,并分别用于倾斜和非倾斜的盘组.

主要成果:

  • 在所有测试的算法中,与倾斜的磁盘相比,在非倾斜的磁盘上训练时,分类模型显示出更高的性能 (更高的AUC).
  • 使用非倾斜光盘数据开发的模型实现了更好的灵敏度和特异性,用于分类光盘病理.
  • 在整个数据集上训练的模型显示,对倾斜光盘的受试者来说,准确性降低了.

结论:

  • 光盘倾斜是一个关键因素,它会对基于深度学习的光盘分类的性能产生负面影响.
  • 光盘分类算法的未来发展需要方法来识别和调整光盘倾斜的影响.