面向数字病理全切片图像分析的高性能数据管理
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
AI总结：
本文提出基于 ADIOS2 的数字病理 WSI 数据管理流水线，通过并行化补丁级 I/O 缩短检索时间，在 CPU 场景获两倍加速，并在 GPU 场景达到与 NVIDIA Magnum IO GDS 相当的性能。
摘要：
在处理全切片成像中的千兆像素数字病理数据时，每次分析操作期间都有相当比例的数据记录具有相关性。例如，在全切片图像（WSI）上部署图像分析算法时，计算瓶颈通常位于输入输出（I/O）系统。由于补丁级处理会给计算机系统带来巨大的 I/O 负载，这一点尤为突出。然而，鉴于不同补丁之间的补丁级图像处理通常具有独立性，该数据管理过程可以进一步并行化。本文详细介绍了我们通过实施 Adaptable IO System version 2（ADIOS2，自适应输入输出系统第2版）来应对这一数据访问挑战的努力。我们的重点是使用 ADIOS2 构建并发布一条以数字病理为中心的流水线，以促进跨 WSI 的简化数据管理。此外，我们还制定了旨在缩短数据检索时间的策略。性能评估涵盖两个关键场景：（1）纯基于 CPU 的图像分析场景（“CPU 场景”）；（2）基于 GPU 的深度学习框架场景（“GPU 场景”）。我们的研究结果揭示了值得注意的成果。在 CPU 场景下，与暴力方法相比，ADIOS2 展现出令人瞩目的两倍加速。在 GPU 场景下，其性能与尖端 GPU I/O 加速框架 NVIDIA Magnum IO GPU Direct Storage（GDS）相当。据我们所知，这似乎是 ADIOS2 在数字病理领域中的首批应用实例之一（如果确有先例的话）。源代码已公开发布于 https://github.com/hrlblab/adios。
英文摘要：
When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we've developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario"). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
