血液图像:使用公共和临床数据集对数字血液电影中的爆发检测视觉变压器进行基准测试
Concetta Piazzese1,2,3, Sophie Williams1,2,3, Gregory Slabaugh3
1Barts Life Sciences, Barts Health NHS Trust, London, United Kingdom.
Journal of pathology informatics
|December 24, 2025
概括
使用视觉转换器 (ViT) 自动化爆细胞检测显著提高了白血病诊断的准确性. 这种机器学习方法为手动血液膜分析提供了更快,更可靠的替代方案,特别是在高通量设置中.
科学领域:
- 血液学 血液学 血液学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 白血病诊断依赖于手动血液膜分析,这是耗时和主观的.
- 在血液膜中检测罕见的爆细胞是一个重大的诊断挑战.
- 自动化这个过程可以提高癌症诊断的速度和准确性.
研究的目的:
- 为了对视觉转换器 (ViT) 进行基准测试,用于在数字化血液膜中自动检测爆细胞.
- 评估ViT模型在公共和临床数据集中的通用性.
- 通过各种培训和预处理策略来优化ViT性能.
主要方法:
- 研究了不同的训练策略 (hold-out/k-fold交叉验证) 和优化器 (Adam,SGD).
- 采用数据预处理技术,如数据增强和高斯金字塔降低样本.
- 在公共 (ALL-IDB) 和来自巴茨健康NHS信托的临床数据集上测试了ViT模型.
主要成果:
- 与Adam一起训练的模型超过了与SGD一起训练的模型.
- 最好的模型,ViT2-Adam,实现了高精度 (≥0.86) 和AUROC (≥0.95).
- 性能超过了其他随机模型,表明了临床潜力.
结论:
- 视觉变压器是可行的临床集成在血液膜分析.
- 数据增强,先进的分割和缩小样本改善了模型的概括性.
- ViT为资源有限和高通量诊断提供了一个有希望的策略.
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