Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.
Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.
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DOI：
10.1158/1541-7786.mcr-21-0665
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发表时间：
2022-03
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癌症研究中的成像数据集在数量和信息密度上都呈指数级增长。这些海量数据集可能会为癌症研究和临床护理提供洞察力，但前提是研究人员必须配备工具，以利用机器学习和人工智能等先进的计算分析方法。在这项工作中，我们强调了三个主题来指导此类计算工具的开发：可伸缩性、标准化和易用性。然后，我们将这些原则应用于开发PathML，这是一个计算病理学的通用研究工具包。我们描述了PathML框架的设计，并演示了在不同用例中的应用。PathML可在www.pathml.com上公开获得。
Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.