"PathOrchestra"是一个计算病理学的综合基础模型,包含100多个不同的临床级任务
Fang Yan1, Jianfeng Wu2, Jiawen Li3
1Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China. yanfang@pjlab.org.cn.
一个多功能病理学基础模型PathOrchestra在各种计算病理学任务中实现了高精度. 这种人工智能模型证明了临床准备好将大规模的自我监督学习整合到数字医学中.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 数字病理学数字病理学
背景情况:
- 高分辨率的病理图像给计算病理学带来了挑战.
- 人工智能基础模型需要大量的数据,存储和计算资源.
- 临床验证对于病理学中的AI模型至关重要.
研究的目的:
- 为了介绍PathOrchestra,一个多功能病理学基础模型.
- 为了评估其在广泛的计算病理学任务上的表现.
- 评估其临床准备和数字医学潜力.
主要方法:
- 在287,424个幻灯片上训练有素的PathOrchestra,来自三个中心的21种组织类型.
- 评估了来自61个私人和51个公共数据集的112个任务的模型.
- 评估任务的性能,包括预处理,分类,预测和报告生成.
主要成果:
- 在47个任务中获得了>0.950的准确性,包括泛癌分类和淋巴瘤亚型.
- 在整个幻灯片和感兴趣区域图像上表现出高性能.
- 第一个用于生成结直肠癌和淋巴瘤结构化报告的模型.
结论:
- "PathOrchestra"显示了对大规模,自我监督的病理学基础模型的临床准备.
- 该模型具有很高的准确性,并有可能整合到数字医学中.
- 突出了AI在临床应用的病理学方面的进步.
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