桥梁放射学和病理学:临床临床的领域通用跨模式学习
Xiang Zhong1, Zhuo Gu1, Manimurugan Shanmuganathan2
1Department of General Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
NPJ digital medicine
|February 16, 2026
概括
这项研究引入了一个新的AI框架,整合了乳腺造影和组织病理学,以改善乳腺癌诊断. 人工智能在各个机构中表现出卓越的准确性和概括性,增强了诊断决策支持.
科学领域:
- 医疗成像中的人工智能
- 计算病理学计算病理学
- 多式联络深度学习多式联络深度学习
背景情况:
- 乳腺癌的临床解释依赖于整合各种成像方式.
- 当前的人工智能系统往往受到单一模式限制和缺乏跨机构通用化的困扰.
- 对人工智能驱动的诊断来说,弥合乳房扫描和组织病理学数据仍然是一个挑战.
研究的目的:
- 为乳腺癌诊断开发一个统一的跨模式人工智能框架.
- 增强跨不同机构和成像数据的AI泛化.
- 提高AI诊断预测的可解释性和临床相关性.
主要方法:
- 使用了一个共享视觉转换器编码器,配有模式特定的适配器.
- 弱监督的对比对齐学习了无像素级标签的交叉模式对应.
- 使用了域泛化策略 (MixStyle,不变风险最小化) 和因果测试时间适应.
- 该模型进行了联合分类,本地化和分级,生成了可解释性的注意力地图.
主要成果:
- 该框架在4个公共基准上实现了0.90的平均AUC,在离开一个域的评估下.
- 它的性能超过了最先进的单模,多模和域泛化基线.
- 与基线 (0.06-0.10) 相比,观察到显著较小的域间隙 (0.03).
- 可视化证实了预测与临床上有意义的特征之间的对齐,提高了透明度.
结论:
- 拟议的统一跨模式框架通过整合乳腺造影和组织病理学,显著改善了乳腺癌诊断.
- 人工智能在各个机构中表现出强大的泛化,并提供可解释的,临床相关的预测.
- 这项工作推进了多式联络人工智能集成和可解释性,为临床部署的诊断决策支持系统铺平了道路.
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