在计算病理学中,可重复使用的样本级推断
Jakub R Kaczmarzyk1,2,3, Rishul Sharma1,4, Peter K Koo1,2
1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
ArXiv
|January 27, 2025
概括
在计算病理学中,SpinPath通过提供预训练的模型和工具来实现样本级深度学习的民主化. 这个工具包加速了对病理学任务的先进深度学习的研究和采用.
科学领域:
- 计算病理学计算病理学
- 深度学习是一种深度学习.
- 医学中的人工智能.
背景情况:
- 基金会模型对计算病理学任务有希望.
- 基于基础模型的样本级模型并不广泛使用,这限制了研究实用性.
- 需要可访问的工具来利用基础模型进行标本级病理学分析.
研究的目的:
- 开发SpinPath,这是一个工具包,用于在计算病理学中民主化样本级深度学习.
- 为研究人员提供预训练的标本级模型的动物园.
- 为了使病理学更容易进行实验和采用深度学习.
主要方法:
- 开发了SpinPath,一个工具包,包括预训练的样本级模型,Python推理引擎和JavaScript推理平台.
- 评估了SpinPath在转移检测任务中的实用性.
- 在九个不同的基础模型中进行了测试.
主要成果:
- 在转移检测任务中,SpinPath成功地证明了它的实用性.
- 该工具包提供了一组多样化的预训练样本级模型.
- Python 和 JavaScript 平台促进了可访问的推断.
结论:
- 在计算病理学中,SpinPath使样本级深度学习民主化.
- 该工具包可以促进可重现性和简化实验.
- 预计SpinPath将加速在病理学研究中采用样本级深度学习.
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