不完美的注释对CNN培训和绩效的影响,例如数字病理学的细分和分类
Laura Gálvez Jiménez1, Christine Decaestecker2
1Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, Brussels, Belgium.
Computers in biology and medicine
|May 26, 2024
概括
数字病理学的杂注释可以降低深度学习模型的性能. 一个小而准确的验证集和预训练是防止过度装配和在核检测,细分和分类中保持高精度的关键.
科学领域:
- 数字病理学数字病理学
- 计算生物学是一种计算生物学.
- 机器学习在医疗保健中的应用
背景情况:
- 细胞核的准确细分和分类对于从组织病理学图像诊断疾病至关重要.
- 深度学习模型需要大型,高质量的注释数据集,这些数据集的创建是困难和耗时的.
研究的目的:
- 调查噪音注释对卷积神经网络 (CNN) 模型性能对核的检测,细分和分类的影响.
- 确定最佳的培训策略,特别是培训时代的数量,以减轻噪音标签的过度适应.
主要方法:
- 训练了一种最先进的CNN模型,对具有不同程度的噪音注释的组织病理图像进行了训练.
- 使用一个小的,精心注释的验证集来评估模型性能.
- 研究了预训练对模型强度的影响.
主要成果:
- 杂的注释显著降低了CNN模型对核分析的性能.
- 使用一个小而干净的验证集可以有效地防止过度适应注释噪声.
- 预训练CNN模型在提高强度和整体性能方面发挥了有益的作用.
结论:
- 一个小的,正确注释的验证集对于训练数字病理学的强大的深度学习模型至关重要.
- 预训练可以提高模型的性能和抗噪音注释的弹性,在图像分析中.
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