标题： 可重复的样本级推理在计算病理学中 显示英文标题

标题： Reusable specimen-level inference in computational pathology

摘要： 基于基础模型的计算病理学已显示出在标本级任务中的巨大潜力，并且越来越容易被研究人员使用。 然而，基于这些基础模型构建的标本级模型仍然大多不可用，这阻碍了它们更广泛的应用和影响。 为了解决这一差距，我们开发了SpinPath，这是一个工具包，旨在通过提供一系列预训练的标本级模型、基于Python的推理引擎和基于JavaScript的推理平台，使标本级深度学习民主化。 我们在九个基础模型的转移检测任务中展示了SpinPath的实用性。 SpinPath可能促进可重复性，简化实验，并加速标本级深度学习在计算病理学研究中的采用。 显示英文摘要

摘要： Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built on these foundation models remain largely unavailable, hindering their broader utility and impact. To address this gap, we developed SpinPath, a toolkit designed to democratize specimen-level deep learning by providing a zoo of pretrained specimen-level models, a Python-based inference engine, and a JavaScript-based inference platform. We demonstrate the utility of SpinPath in metastasis detection tasks across nine foundation models. SpinPath may foster reproducibility, simplify experimentation, and accelerate the adoption of specimen-level deep learning in computational pathology research.