构建和验证用于预培训的一般医学图像数据集
Rongguo Zhang1, Chenhao Pei2, Ji Shi3
1Academy for Multidisciplinary Studies, Capital Normal University, 105 West Third Ring Road North, Haidian District, Beijing, China. zrongguo@cnu.edu.cn.
Journal of imaging informatics in medicine
|August 15, 2024
概括
一个新的医学图像数据集 (CPMID) 能够为医学图像分析中的深度学习模型提供优质的转移学习. 与Image.Net相比,对CPMID的预训显著提高了分类和细分性能.
科学领域:
- 深度学习是一种深度学习.
- 医疗图像分析 医学图像分析
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 目前用于医学成像的深度学习模型通常从头开始训练或使用ImageNet预训练.
- 缺乏普遍接受的医学图像数据集用于预训练.
- 转移学习提高模型性能,但需要适当的预训练数据集.
研究的目的:
- 为模型预训练构建一个通用医疗图像数据集.
- 为下游任务验证在此数据集上预训练的模型的有效性.
- 建立医学图像分析预培训的新标准.
主要方法:
- 收集并策划了几个公共医疗图像数据集,以形成CPMID数据集.
- 使用CPMID数据集的预训练的复网和视觉变压器模型.
- 对医疗图像分类和细分任务进行预训练模型的评估,与scratch和ImageNet预训练进行比较.
主要成果:
- 与 ImageNet 预训练的模型相比,在 CPMID 上预训练的模型实现了更高的分类准确性和 ROC-AUC 值.
- 观察到平均分类准确度的提高为4.30%,8.86%和3.85%.
- 在分类和细分任务中表现出最佳的性能和效率平衡.
结论:
- 构建的CPMID数据集及其相关预训练模型对于医学图像分析非常有效.
- 基于CPMID的预培训比用于医学成像任务的ImageNet预培训具有显著的优势.
- 这项工作为推进医学图像分析中的深度学习提供了宝贵的资源.
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