Guiding humans to create better labeled datasets for machine learning in biomedical research

摘要 数字和计算病理学中使用的全切片图像以平铺金字塔格式存储， 支持平滑可视化。虽然有许多工具可以读取这些图像，但缺乏 有足够的工具可以方便和快速地将数据转换为这些平铺的金字塔格式。这限制了 研究者通过使用病理学查看图像分析或其他可视化结果的能力 软件工具。这导致病理学软件工具和通用 用于数据和图像分析的软件工具，如Numpy。在本提案中，我们将创建优化和易于- 使用开放源代码编程接口，允许从各种 流行的数组和矢量数据格式。这将允许用户创建任意大的平铺金字塔图像 从Numpy，Zarr和Dask数组，以及可缩放矢量图形等矢量格式。首先，我们将生成 并记录了用于Python的模块化和通用平铺接口。第二，我们将实施 支持最流行的输入和输出格式。第三，我们将注重软件工程， 该软件是可维护的，并可由研究社区扩展。这包括以下文件： 代码和使用示例，实现测试和代码审查，以及包管理器和 云就绪总之，这将使研究人员能够更好地可视化他们的分析结果， 更好地整合数字病理学软件和通用科学和 数据分析软件。

ABSTRACT The whole-slide images used in digital and computational pathology are stored in a tiled pyramidal format to support smooth visualization. While there are a number of tools to read these images, there is a lack of adequate tools available for easy and fast conversion of data to these tiled pyramidal formats. This limits the ability of investigators who generate image analysis or other visualizations from viewing these using pathology software tools. This has resulted in a disconnect between pathology software tools and general-purpose software tools for data and image analysis like Numpy. In this proposal we will create optimized and easy-to- use open-source programming interfaces that allow generation of tiled pyramidal images from a variety of popular array and vector data formats. This will allow users to create arbitrarily large tiled pyramidal images from Numpy, Zarr, and Dask arrays, and vector formats like Scalable Vector Graphics. Firstly, we will generate and document a modular and general-purpose tiling interface for use in python. Second, we will implement support for the most popular input and output formats. Third, we will focus on software engineering to ensure that the software is maintainable and extensible by the research community. This includes documentation of code and examples for use, implementing testing and code review, and packaging for package managers and cloud-readiness. Altogether, this will allow investigators to better visualize the results of their analyses, and will better integrate the now disconnected domains of digital pathology software and general purpose scientific and data analysis software.