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
{"title":"MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis","authors":"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","doi":"arxiv-2311.10443","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence methods are powerful tools for biological image\nanalysis and processing. High-quality annotated images are key to training and\ndeveloping new methods, but access to such data is often hindered by the lack\nof standards for sharing datasets. We brought together community experts in a\nworkshop to develop guidelines to improve the reuse of bioimages and\nannotations for AI applications. These include standards on data formats,\nmetadata, data presentation and sharing, and incentives to generate new\ndatasets. We are positive that the MIFA (Metadata, Incentives, Formats, and\nAccessibility) recommendations will accelerate the development of AI tools for\nbioimage analysis by facilitating access to high quality training data.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"74 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.10443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.