视觉化放射性连接:一个可解释的粗细基础模型与多视图乳房造影和相关报告
Yuan Gao1,2,3, Hong-Yu Zhou4, Xin Wang1,2,3
1GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, P. Debyelaan 25, 6202 AZ, Maastricht, the Netherlands.
Radiology. Artificial intelligence
|December 17, 2025
概括
数字造乳镜的新预训练方法通过将图像和报告联系起来,改善了乳腺癌的检测. 这种人工智能模型可以减少37%的假阳性,并使零射击细分成为可能,从而提高诊断准确性.
科学领域:
- 医疗成像中的人工智能
- 数字乳房摄影分析分析
- 计算机辅助的乳腺癌检测
背景情况:
- 数字造乳镜可以从图像和报告中生成复杂的数据.
- 标签限制的设置对训练强大的AI模型构成挑战.
- 提取细粒度的视觉语言表示对于准确的分析至关重要.
研究的目的:
- 开发一个基础的预训练方法,用于数字造乳镜.
- 从乳房影像和报告中提取细粒度的视觉语言表示.
- 改进在标签有限的场景中进行自动化乳腺癌分析.
主要方法:
- 开发了一种用于自动化乳腺癌分析的多视图乳房造影-报告预训框架.
- 整合了一种针对密集纤维腺组织的异常感知技术.
- 在四个外部医疗中心评估了恶性瘤分类,细分和局部化任务的框架.
主要成果:
- 该模型成功地捕获了多视图乳房造影和报告之间的关系,如可视化结果所示.
- 将乳腺癌的错误阳性结果减少了37%,并使零射击异常细分成为可能.
- 在恶性瘤分类 (例如,INbreast AUC:0.90与0.78) 和细分/局部化 (例如,INbreast Dice:0.75与0.63) 方面表现优于现有的方法.
结论:
- 拟议的框架提高了数字造乳镜的解释性.
- 它允许精细的多式联络基础学习,用于多视图乳房造影和报告.
- 这种方法显示了改善人工智能驱动的乳腺癌诊断的巨大潜力.
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