[论文解读] Enabling Global Image Data Sharing in the Life Sciences

Peter Bajcsy, Sreenivas Bhattiprolu+21 more|arXiv (Cornell University)|Jan 23, 2024

Scientific Computing and Data Management被引用 5

一句话总结

本白皮书概述全球生物医学图像数据共享的常见与新兴用例，讨论所需的框架（技术、法律、伦理、资源配置），并提出在十年内实现理想共享数据生态系统的路径。

ABSTRACT

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

研究动机与目标

• 促成协调合作，以实现全球生命科学领域图像数据共享的价值与基础设施。
• 识别生物医学图像数据的常见与新兴用例，包括基于 AI 的分析。
• 讨论用于共享图像数据框架的技术、资源、法律与伦理要求。
• 阐明一个理想的未来情景以及对机构、社区、资助者和出版商的实际措施。

提出的方法

• 介绍与生命科学相关的图像数据和 AI 应用的常见与新兴用例。
• 讨论实现大规模共享所需的技术、资源、法律与伦理方面。
• 勾勒全球图像数据共享的理想世界情景及其在生命科学各领域的收益。
• 提出针对机构、成像社区、数据倡议和国家资助方的具体行动。
• 将用于协调生成与预出版托管的配套论文视为更广泛努力的一部分。

实验结果

研究问题

• RQ1 在生物学与医学中，哪些是对共享图像数据影响最大的用例？
• RQ2 为实现全球图像数据共享需要哪些技术、法律、伦理和资源框架？
• RQ3 在未来十年全球共享的理想图像数据生态系统将是什么样子？
• RQ4 机构、社区、资助者和出版商可以采取哪些具体措施来推进这一目标？

主要发现

• 识别将从共有图像数据与 AI 技术中受益的常见与新兴用例。
• 关于数据共享在技术、资源、法律和伦理维度上所需的框架的讨论。
• 对于在十年内向全球研究人员开放的开放图像数据集的理想世界愿景。
• 面向机构、成像社区、数据倡议、国家资助机构和出版商的具体措施，以提升就绪度和托管能力。