基准分析病理学基础模型:适应策略和情景
Jaeung Lee1, Jeewoo Lim1, Keunho Byeon1
1School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea.
Computers in biology and medicine
|April 3, 2025
概括
这项研究对病理学基础模型进行了基准测试,发现对各种数据集有效的参数高效微调和对计算病理学中数据有限的场景有益的少数镜头方法.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能.
- 数字病理学图像分析 数字病理学图像分析
背景情况:
- 基础模型对病理图像分析有希望,但面临着因数据多样化和可用性有限而面临的适应挑战.
- 在不同的数据源,获取条件和下游任务中调整这些模型需要强大的基准测试.
研究的目的:
- 在20个数据集中对四个病理特异性基础模型进行基准测试.
- 在不同的数据条件下,评估模型在一致性和灵活性场景中的性能.
- 为在临床病理学环境中部署基础模型提供指导.
主要方法:
- 在20个数据集上对四个基础模型进行比较,使用一致性和灵活性评估场景.
- 评估五种微调方法,以适应分类任务中的各种数据集.
- 评估了五种用于在数据有限环境中的性能,用于幻灯片级生存预测的几次性学习方法.
主要成果:
- 参数高效微调在分类任务中证明了模型适应不同数据集的效率和有效性.
- 基础模型在生存预测中的表现受特征聚合和数据特征的影响.
- 只有在测试阶段修改的少量射击学习方法在数据有限的场景中对基础模型显示出更大的好处.
结论:
- 参数高效微调是适应病理基础模型的可行策略.
- 特性聚合和数据特征对于生存预测任务至关重要.
- 特定的少量学习方法在低数据环境中增强了基础模型的实用性,指导了临床部署.
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