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

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 Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica Riesterer, Norman Rzepka, Ugis Sarkans, Beatriz Serrano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley
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引用次数: 0

摘要

人工智能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方法的关键,但是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。我们将社区专家聚集在一个研讨会上,制定指导方针,以改善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MIFA(元数据、激励、格式和可访问性)建议将促进高质量训练数据的访问,从而加速生物图像分析人工智能工具的开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
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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