推进自动胃炎诊断:一种可解释的多标签深度学习框架,用于同时评估多个指标
Mengke Ma1, Xixi Zeng1, Linhao Qu1
1Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China.
The American journal of pathology
|May 18, 2024
概括
一个名为AMMNet的AI模型使用幻灯片级数据准确诊断胃炎指标,如炎症和缩. 它提高了初级病理学家的准确性和效率,减少了诊断时间,提高了解释性.
科学领域:
- 数字病理学数字病理学
- 医学中的人工智能
- 胃炎的诊断 胃炎的诊断
背景情况:
- 胃炎的准确诊断依赖于评估形态特征,如炎症,胃缩和肠道转化等.
- 目前人工智能 (AI) 对包括胃炎在内的非瘤疾病的应用有限.
- 现有的深度学习模型往往忽略了关键的形态指标,缺乏同时诊断能力或可解释的输出.
研究的目的:
- 开发一个基于注意力的多级多标签学习网络 (AMMNet),用于同时对胃炎指标进行可解释的诊断.
- 通过评估AMMNet对初级病理学家诊断准确性和效率的影响,评估AMMNet在现实环境中的表现.
- 在多中心数据集中验证AMMNet的有效性.
主要方法:
- 开发AMMNet,一个基于注意力的多实例多标签学习网络.
- 使用幻灯片级弱标签来训练模型诊断活动,缩和肠道代谢.
- 设计了一项涉及初级病理学家的诊断测试,有或没有AMMNet的协助.
主要成果:
- 在评估活性 (AUC,0.93),缩 (AUC,0.97) 和肠道代谢 (AUC,0.93) 方面,AMMNet表现出很高的性能.
- 观察到较低的虚假阴性率:活性为0.04,缩为0.08,肠道转化为0.18.
- 通过AMMNet的帮助,初级病理学家的假阴性率降低,每张整张幻灯片图像的处理时间从5.46分钟减少到2.85分钟.
- 区块级聚类分析提供了相关图像区域的可解释可视化.
结论:
- 通过使用多中心数据,AMMNet有效和准确地评估胃炎诊断的关键形态指标.
- 人工智能模型显著提高了病理学家,特别是初级病理学家的诊断准确性和效率.
- 多实例多标签的学习策略显示出支持常规诊断病理学的前景,并要求进一步调查.
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