专业领域的通用主义模型:评估对比的语言图像预训练在脑MRI中零射击异常检测
Aldo Marzullo1, Nicolò Cappa2, Matteo Morellini2
1IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, Milan, Italy. aldo.marzullo@polimi.it.
Journal of medical systems
|November 10, 2025
概括
零射击异常检测 (ZSAD) 模型对识别大脑异常具有前景,但需要对临床可行性进行特定域微调. 当前的2D方法在3D医学成像中面临着空间一致性挑战.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 零射击异常检测 (ZSAD) 提供了一种在没有特定任务监督的情况下识别医疗异常的方法.
- 最初用于工业用途的基于CLIP的ZSAD模型正在被用于医疗应用.
研究的目的:
- 为了对基于CLIP的最先进的ZSAD模型进行基准测试,以检测大脑转移.
- 使用BraTS-MET数据集在有限或没有监督下评估模型性能.
- 评估特定域微调对医学成像中ZSAD性能的影响.
主要方法:
- 基准测试基于CLIP的通用和医疗适应的ZSAD模型.
- 使用BraTS-MET数据集来检测大脑转移.
- 用最小的标记数据在各种训练范式中评估模型.
主要成果:
- 基于CLIP的ZSAD模型的准确性有限,特别是在外围大脑区域.
- 域特定的微调产生了实质性的性能增长,尽管仍然低于最佳.
- 基于2D的方法对3D医学成像问题的空间一致性有局限性.
结论:
- 目前基于CLIP的ZSAD模型需要对医学成像中的临床使用进行重大调整.
- 在2D到3D适应中解决空间一致性问题对于提高ZSAD可行性至关重要.
- 进一步研究特定领域微调策略是必要的,以提高ZSAD的性能.
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