深度学习用于对炎症性肠病活动的分类 整张幻灯片 结肠组织病理学图像
Amit Das1, Tanmay Shukla2, Naofumi Tomita2
1Department of Computer Science, Dartmouth College, Hanover, New Hampshire.
The American journal of pathology
|January 12, 2025
概括
一个深度学习模型从整个幻灯片图像中准确地评分炎症性肠病 (IBD) 活动,解决专家病理学家可用性和观察者间变异性方面的挑战,以提高诊断一致性.
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:
- 炎症性肠病 (IBD) 活动的组织病理学评分面临挑战,原因是专家病理学家的有限性和观察者之间的变异性.
- 标准化评估对于有效的IBD管理和治疗决策至关重要.
研究的目的:
- 开发和验证一种深度学习模型,用于从血素和素染色全片图像 (WSIs) 来分类IBD活动等级.
- 为通用病理学家提供一个强大的工具,提高IBD评估的一致性和效率.
主要方法:
- 从636名IBD患者中利用了2077个WSI,由专家胃肠道病理学家分为四个活动等级.
- 开发并验证了一个基于变压器的深度学习模型,使用五倍交叉验证.
- 使用HoVer-Net分析了中性粒细胞分布,并使用注意力图可视化了模型预测.
主要成果:
- 深度学习模型实现了曲线下的加权平均面积为0.871.1.
- 显示出强大的性能,加权平均精度 (0.695),回忆 (0.697) 和F1得分 (0.695).
- 在活动等级之间观察到中性粒细胞分布的显著差异,注意力图显示了可解释性的潜力.
结论:
- 开发的深度学习模型表现出强大的诊断性能,用于分类IBD活动.
- 这种基于人工智能的方法可能会提高病理学家对IBD评估的一致性和效率.
- 注意地图为模型预测提供了洞察力,有助于IBD数字病理学的解释性.
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