打造可复用的病理切片级推理工具，加速医学影像深度学习应用
Reusable specimen-level inference in computational pathology
- 提供预训练切片级模型库与跨平台推理引擎
- 在9个基础模型上验证了转移灶检测效果
- 适合希望快速部署病理分析模型的研究者
计算病理学领域的基础模型在切片级任务中展现出巨大潜力，且日益为研究者所获取。然而，基于这些基础模型构建的切片级模型仍大多不可用，限制了其广泛影响。为此，我们开发了SpinPath，一个旨在普及切片级深度学习的工具包，包含预训练切片级模型库、基于Python的推理引擎和基于JavaScript的推理平台。我们在九个基础模型上展示了SpinPath在转移灶检测任务中的有效性。该工具可促进研究可复现性，简化实验流程，并加速切片级深度学习在计算病理学研究中的应用。
原文摘要 · Abstract (English)
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.
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