计算机科学
标杆管理
重新使用
钥匙(锁)
介绍(产科)
数据共享
数据科学
数据收集
人工智能应用
数据存取
激励
最佳实践
图像自动标注
协议(科学)
知识共享
万维网
图像共享
情报检索
人工智能
可视化
作者
Teresa Zulueta-Coarasa,Florian Jug,Aastha Mathur,Josh Moore,Arrate Muñoz‐Barrutia,Liviu Anita,Kola Babalola,Peter Bankhead,Perrine Paul‐Gilloteaux,Nodar Gogoberidze,Martin L. Jones,Gerard J. Kleywegt,Paul K Korir,Anna Kreshuk,A. Yoldaş,Luca Marconato,Kedar Narayan,Nils Norlin,Buğra Özdemir,Jessica L. Riesterer
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2025-09-15
卷期号:22 (11): 2245-2252
被引量:3
标识
DOI:10.1038/s41592-025-02835-8
摘要
Artificial intelligence (AI) methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new algorithms, but access to such data is often hindered by the lack of standards for sharing datasets. We discuss the barriers to sharing annotated image datasets and suggest specific 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 sure that the Metadata, Incentives, Formats and Accessibility (MIFA) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high-quality training and benchmarking data.
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