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IPATH:来自Instagram的大规模病理图像文本数据集用于视觉语言模型培训
S Mirhosseini1, T Rai2,3, P Diaz-Santana4
1Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK. contact@erfan.uk.
Journal of imaging informatics in medicine
|January 23, 2026
概括
研究人员从Instagram图像创建了IPATH数据集,以训练AI用于病理学. 这种AI模型,IP-CLIP,显示出强大的诊断准确性,改善了医疗图像分析.
科学领域:
- 人工智能的人工智能
- 计算病理学计算病理学
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 可以检测病理图像中的微妙模式,提高诊断准确度.
- 人工智能在病理学领域的发展的一个重要障碍是大量的,公开可用的,注释图像数据集的稀缺性.
- 社交媒体平台为医疗图像数据提供了一个潜在的,但尚未探索的来源.
研究的目的:
- 为人工智能模型培训策划一个来自Instagram的病理图像的新型数据集.
- 使用这个精心策划的数据集,开发和评估一个多式联网人工智能模型 (IP-CLIP).
- 展示社交媒体数据在推进医疗图像分析人工智能的实用性.
主要方法:
- 策划了IPATH数据集,包括来自Instagram的45,609个病理图像-文本对,使用自动分类器,大型语言模型和人工过来进行质量控制.
- 通过对IPATH数据集上预先训练的CLIP模型进行微调来开发IP-CLIP.
- 通过使用零射击分类和线性探测评估了IP-CLIP在七个外部遗传病理学数据集上的性能,并通过在持有子集上的检索评估了图像-文本对齐.
主要成果:
- 在零射击分类和线性探测任务中,IP-CLIP在外部数据集上的原始CLIP模型的表现始终优于IP-CLIP.
- 尽管在较小的数据集上进行训练,但IP-CLIP的性能与最先进的病理学视觉语言模型相当或超过.
- 与CLIP和在检索任务中的专用模型相比,IP-CLIP表现出优异的图像-文本对齐.
结论:
- 来自Instagram的IPATH数据集是开发AI用于医学图像分类的宝贵资源.
- 利用社交媒体数据可以有效地解决人工智能培训中注释医学图像的稀缺问题.
- 开发的IP-CLIP模型显示了提高病理学诊断准确性和决策支持的巨大潜力.
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