元数据
计算机科学
介绍(产科)
激励
重新使用
数据共享
数据科学
万维网
钥匙(锁)
质量(理念)
数据元素
元数据建模
情报检索
工程类
认识论
放射科
哲学
病理
经济
微观经济学
替代医学
医学
废物管理
计算机安全
作者
eresa Zulueta-Coarasa,Florian Jug,Aastha Mathur,Josh Moore,Arrate Muñoz‐Barrutia,Liviu Anita,Kola Babalola,Pete Bankhead,Perrine Gilloteaux,Nodar Gogoberidze,Martin Jones,Gerard J. Kleywegt,Paul K Korir,Anna Kreshuk,A. Yoldaş,Luca Marconato,Kedar Narayan,Nils Norlin,Bugra Oezdemir,Jessica L. Riesterer
标识
DOI:10.48550/arxiv.2311.10443
摘要
Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.
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