Enhancing CBTN digital pathology processing pipeline through the use AWS cloud-based services to enable automation, parallel processing, and rapid use of AI/ML analytics

项目总结 加布里埃拉·米勒儿童首个儿科研究计划是一项合作倡议，目标是 了解儿科疾病的病因和驱动因素。相关工作包括(1)开发数据驱动 平台、工作流程和工具；(2)加速发现一般原因和共享的生物途径 在条件内和跨条件下；以及(3)使科学发现能够快速转化为个性化 治疗。通过这个计划，已经产生了一些最大的、多模式的儿科数据集， 协调，并发布供研究界使用。同时，开发的软件平台也有 在云环境中实现简化、用户友好的工作流，以支持数据探索和分析。 虽然这项工作极大地促进了综合数据集的使用，特别是在基因组学和临床上 数据方面，医学成像数据的相关进展一直较慢。在本项目中，我们将重点放在 研究环境中的数字病理数据，旨在支持使用现代云基础设施和 儿童首张数字病理幻灯片流程。具体来说，我们将探索、实施、优化和评估 云平台和服务，以减少当前在数据接收和发布以及准备数字产品方面的挑战 用于下游分析的幻灯片图像。预计选定的云解决方案将启用工作流 自动化、计算资源扩展和对整个数据生命周期的全面支持。这应该会极大地 降低跨研究使用儿童优先数字病理数据的运营和计算成本 上下文。总而言之，该项目将数字病理数据和工具与高性能云连接起来 通过高级分析和面向所有儿童的多模式集成，加快工作空间的使用 儿科数据。

PROJECT SUMMARY The Gabriella Miller Kids First pediatric research program is a collaborative initiative with the goal of understanding the etiology and drivers of pediatric diseases. Related efforts include (1) developing data-driven platforms, workflows and tools; (2) accelerating discovery of generic causes and shared biologic pathways within and across conditions; and (3) enabling rapid translation of scientific discoveries to personalized treatments. Through this program, some of the largest, multi-modal pediatric datasets have been generated, harmonized, and released for use by the research community. In parallel, developed software platforms have enabled streamlined, user-friendly workflows in cloud environments to empower data exploration to analysis. While this work has significantly advanced the use of integrated datasets, particularly for genomics and clinical data, related advancements for medical imaging data have been slower. In the present project, we focus on digital pathology data in research contexts and aim to enable the use of modern cloud infrastructure and processes for Kids First digital pathology slides. Specifically, we will explore, implement, optimize, and assess cloud platforms and services to reduce current challenges in data ingest and release, and preparation of digital slide images for downstream analytics. The selected cloud solutions are expected to enable workflow automation, scaling of computing resources, and full support of the entire data lifecycle. This should drastically reduce the operational and computational costs of utilizing Kids First digital pathology data across research contexts. Together, this project will bridge digital pathology data and tools with high-performance cloud workspaces to accelerate their use with advanced analytics and multi-modal integration for all Kids First pediatric data.