利用 WSInfer 和 QuPath 实现病理学开放可复用的深度学习
Open and reusable deep learning for pathology with WSInfer and QuPath
AI总结：
本文提出开源软件生态系统 WSInfer，通过 Python 工具、QuPath 扩展和标准化模型库，使病理学家无需编程经验即可对全切片图像应用深度学习模型，促进模型的广泛复用与探索。
摘要：
近年来，数字病理学领域涌现出大量深度学习模型。尽管取得了显著进展，其他研究者和病理学家仍然很少能够获取文献中发表的模型并将其应用于自己的图像。这主要是由于模型共享和运行两方面都存在困难。为了解决这些问题，我们推出了 WSInfer：一个全新的开源软件生态系统，旨在使病理学深度学习更加高效便捷。WSInfer 包含三个主要组成部分：1) 一个 Python 包和命令行工具，用于高效地对全切片图像应用基于图像块的深度学习推理；2) 一个 QuPath 扩展，通过用户友好且可交互的软件提供替代推理引擎；3) 一个模型库，使病理学模型及其元数据能够以标准化形式轻松共享。这些贡献共同旨在通过将模型交到病理学家手中，并在通过 QuPath 访问时消除对编程经验的需求，从而鼓励出于研究目的对深度学习模型进行更广泛的复用、探索和审视。WSInfer 源代码托管在 GitHub 上，文档可在 https://wsinfer.readthedocs.io 获取。
英文摘要：
The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologists to be able to access models published in the literature and apply them to their own images. This is due to difficulties in both sharing and running models. To address these concerns, we introduce WSInfer: a new, open-source software ecosystem designed to make deep learning for pathology more streamlined and accessible. WSInfer comprises three main elements: 1) a Python package and command line tool to efficiently apply patch-based deep learning inference to whole slide images; 2) a QuPath extension that provides an alternative inference engine through user-friendly and interactive software, and 3) a model zoo, which enables pathology models and metadata to be easily shared in a standardized form. Together, these contributions aim to encourage wider reuse, exploration, and interrogation of deep learning models for research purposes, by putting them into the hands of pathologists and eliminating a need for coding experience when accessed through QuPath. The WSInfer source code is hosted on GitHub and documentation is available at https://wsinfer.readthedocs.io.
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