MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.

IF 32.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods Pub Date : 2025-09-15 DOI:10.1038/s41592-025-02835-8

Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kolawole Babalola, Peter Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin L Jones, Gerard J Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica L Riesterer, Craig Russell, Norman Rzepka, Ugis Sarkans, Beatriz Serrano-Solano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley

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引用次数: 0

Abstract

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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MIFA：元数据、激励、格式和可访问性指南，以改善生物图像分析中人工智能数据集的重用。

人工智能（AI）方法是生物图像分析和处理的有力工具。高质量的注释图像是训练和开发新算法的关键，但由于缺乏共享数据集的标准，对这些数据的访问往往受到阻碍。我们讨论了共享注释图像数据集的障碍，并提出了具体的指导方针，以提高人工智能应用程序对生物图像和注释的重用。其中包括数据格式、元数据、数据表示和共享的标准，以及生成新数据集的激励措施。我们确信，元数据、激励、格式和可访问性（MIFA）建议将促进高质量培训和基准数据的访问，从而加速生物图像分析人工智能工具的发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Methods 生物-生化研究方法

CiteScore

58.70

自引率

1.70%

发文量

326

审稿时长

1 months

期刊介绍： Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.