基于深度学习的双层模型用于Helicobacter胃炎的组织学诊断
Yi-Jyun Lin1, Chi-Chung Chen2, Chia-Hsiang Lee1
1Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan.
Histopathology
|July 31, 2023
概括
一个新的深度学习模型在数字幻灯片上准确诊断Helicobacter pylori (HP) 胃炎. 这种人工智能方法实现了人类水平的准确性,潜在地简化了诊断,减少了对特殊染料的需求.
科学领域:
- 数字病理学数字病理学
- 医学中的人工智能.
- 胃肠病学 胃肠病学
背景情况:
- 在全球范围内,Helicobacter pylori (HP) 感染是慢性胃炎的主要原因.
- 在数字幻灯片上诊断HP是具有挑战性的,因为细菌的小尺寸和图像分辨率有限.
研究的目的:
- 开发和评估基于深度学习的模型,以使用数字幻灯片准确诊断HP胃炎.
- 评估该模型与人类病理学家的性能及其在简化诊断工作流程方面的潜力.
主要方法:
- 创建了一个双层深度学习模型,包括一个全幻灯片模型用于诊断和一个辅助模型用于HP本地化.
- 整个幻灯片模型在885个带有幻灯片级标签的全幻灯片图像 (WSI) 上受过训练.
- 辅助模型被训练在WSIs中的特定区域,以精确定位HP位置.
主要成果:
- 整个幻灯片模型实现了高诊断性能,AUC为0.9739.
- 灵敏度和特异性分别为93.3%和90.1%,相当于病理学家的93.3%和84.2%.
- 辅助定位模型的平均精度为0.5796,用于突出显示HP区域.
结论:
- 一个深度学习模型可以准确地诊断HP胃炎在H&E染色的WSIs上,达到人类水平的性能.
- 开发的双层系统有助于HP本地化和诊断确认.
- 这种人工智能方法可能会缩短诊断时间表,并减少对特殊染色方法的依赖.
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