优化数据增强,用于在深度和轻量级网络中检测骨髓瘤
Waala Gouda1, Sidra Tahir2, Tariq Ali2
1Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt.
Journal of orthopaedics
|February 12, 2026
概括
深度学习模型显示,从组织病理学图像对骨髓瘤 (Ost) 进行分类是有前途的. 适度的数据增强提高了NasMobileNet的性能,达到95%的准确性,而更深层次的模型从增加的增强中受益.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算病理学计算病理学
背景情况:
- 骨髓瘤 (Ost) 是一种侵袭性骨癌,主要影响年轻人.
- 由于瘤异质性和有限的注释数据,Ost的组织病理学分类是困难的.
- 深度学习 (DL) 提供了提高Ost分类准确性的潜力.
研究的目的:
- 系统地评估预处理和数据增强对基于DL的骨髓瘤图像分类的影响.
- 为了比较不同的转移学习模型 (VGG19,InceptionV3,InceptionResNetV2,NasMobileNet) 对于Ost分类的性能.
- 确定最佳的增强策略,以增强DL模型概括在骨髓瘤诊断.
主要方法:
- 使用了来自UT西南/UT达拉斯骨肉瘤数据集的血素和素 (H&E) 染色图像.
- 应用标准化预处理技术,包括降噪和对比度增强.
- 实现受控数据增强,每类生成0,650,1000和1500个合成图像.
- 微调了四种转移学习模型,并使用准确度,灵敏度,特异性和ROC-AUC进行了评估.
主要成果:
- 纳斯移动网络实现了95.07%的准确性,95%的灵敏度和95%的特异性 (AUC=0.96) 与中度增强.
- 像InceptionResNetV2这样的更深层次的模型显示,随着增强的增强,性能得到了改善,达到94.37%的准确性.
- 统计分析 (p > 0.05) 表明模型之间的性能没有显著差异,这表明一致性.
- 发现数据增强的有效性取决于模型.
结论:
- 深度学习,结合系统分析和可解释性,可以提高骨髓瘤分类的可靠性.
- 数据增强策略的选择应根据所使用的特定深度学习模型量身定制.
- 这项研究为优化DL方法的框架提供了一个框架,用于挑战瘤学中的基因病理图像分类任务.
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