Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

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

Zulueta-Coarasa, Teresa ; Jug, Florian ; Mathur, Aastha ; Moore, Josh ; Muñoz-Barrutia, Arrate ; Anita, Liviu ; Babalola, Kola ; Bankhead, Pete ; Gilloteaux, Perrine and Gogoberidze, Nodar , et al. (2023)
Abstract (Swedish)
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... (More)
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.
(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
pages
16 pages
publisher
arXiv.org
DOI
10.48550/arXiv.2311.10443
language
Swedish
LU publication?
yes
additional info
https://arxiv.org/abs/2311.10443
id
d01a5c08-9402-4a2a-98c8-74db6c6fc17a
date added to LUP
2025-08-26 11:23:06
date last changed
2025-08-26 14:02:52
@misc{d01a5c08-9402-4a2a-98c8-74db6c6fc17a,
  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.<br/>}},
  author       = {{Zulueta-Coarasa, Teresa and Jug, Florian and Mathur, Aastha and Moore, Josh and Muñoz-Barrutia, Arrate and Anita, Liviu and Babalola, Kola and Bankhead, Pete and Gilloteaux, Perrine and Gogoberidze, Nodar and Jones, Martin and Kleywegt, Gerard J. and Korir, Paul and Kreshuk, Anna and Küpcü Yoldaş, Aybüke and Marconato, Luca and Narayan, Kedar and Norlin, Nils and Oezdemir, Bugra and Riesterer, Jessica and Rzepka, Norman and Sarkans, Ugis and Serrano, Beatriz and Tischer, Christian and Uhlmann, Virginie and Ulman, Vladimír and Hartley, Matthew}},
  language     = {{swe}},
  month        = {{11}},
  note         = {{Preprint}},
  publisher    = {{arXiv.org}},
  title        = {{MIFA : Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2311.10443}},
  doi          = {{10.48550/arXiv.2311.10443}},
  year         = {{2023}},
}