PathViT:自动疾病分类从骨肌肉组织病理学
Taymaz Akan1, Sait Alp2, Richa Aishwarya3
1Department of Medicine, Louisiana State University Health Sciences Center, Shreveport, Louisiana; Department of Software Engineering, Faculty of Engineering, Istanbul Topkapi University, Istanbul, Turkey.
The American journal of pathology
|November 8, 2025
概括
深度学习模型PathViT准确地区分健康的肌肉纤维和患病的肌肉纤维,改善了骨肌肉病理的诊断速度和一致性. 这种人工智能工具减少了手工分析,增强了生物医学研究和临床应用.
科学领域:
- 生物医学工程 生物医学工程
- 计算病理学计算病理学
- 数字健康数字健康
背景情况:
- 从组织学图像中手动分析骨肌病理是耗时且主观的.
- 手动分析中用户间和用户内部的变化会影响诊断准确性和一致性.
- 目前的方法需要手动的细胞计数,细分和值,增加劳动强度.
研究的目的:
- 开发一种自动化的深度学习模型,PathViT,用于骨肌肉病理学分析.
- 为了减少人为干预,主观性和肌肉纤维分类的变化.
- 与传统的手动方法相比,大大减少了分析时间.
主要方法:
- 采用小麦芽聚氨酸染色和骨肌肉的数字组织病理学.
- 采用基于变压器的深度学习模型 (PathViT) 进行自动分类.
- 将PathViT的性能与最先进的深度学习模型进行比较.
主要成果:
- 在分类健康与患病的肌肉纤维方面,PathViT实现了96%的准确性.
- 该模型在区分肌肉病理方面表现优于其他深度学习模型.
- 通过PathViT,分析的可扩展性提高,可变性降低.
结论:
- PathViT为骨肌病理学分析提供了一个强大的,自动化的解决方案.
- 该模型提高了诊断的准确性和一致性,减少了对手工方法的依赖.
- 在生物医学研究和临床环境中,PathViT具有作为一种有价值的工具的潜力.
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