MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915612265021440 |
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| author | Zulueta-Coarasa, Teresa Jug, Florian Mathur, Aastha Moore, Josh Muñoz-Barrutia, Arrate Anita, Liviu Babalola, Kola Bankhead, Pete Gilloteaux, Perrine Gogoberidze, Nodar Jones, Martin Kleywegt, Gerard J. Korir, Paul Kreshuk, Anna Yoldaş, Aybüke Küpcü Marconato, Luca Narayan, Kedar Norlin, Nils Oezdemir, Bugra Riesterer, Jessica Rzepka, Norman Sarkans, Ugis Serrano, Beatriz Tischer, Christian Uhlmann, Virginie Ulman, Vladimír Hartley, Matthew |
| author_facet | Zulueta-Coarasa, Teresa Jug, Florian Mathur, Aastha Moore, Josh Muñoz-Barrutia, Arrate Anita, Liviu Babalola, Kola Bankhead, Pete Gilloteaux, Perrine Gogoberidze, Nodar Jones, Martin Kleywegt, Gerard J. Korir, Paul Kreshuk, Anna Yoldaş, Aybüke Küpcü Marconato, Luca Narayan, Kedar Norlin, Nils Oezdemir, Bugra Riesterer, Jessica Rzepka, Norman Sarkans, Ugis Serrano, Beatriz Tischer, Christian Uhlmann, Virginie Ulman, Vladimír Hartley, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10443 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 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 Gogoberidze, Nodar Jones, Martin Kleywegt, Gerard J. Korir, Paul Kreshuk, Anna Yoldaş, Aybüke Küpcü Marconato, Luca Narayan, Kedar Norlin, Nils Oezdemir, Bugra Riesterer, Jessica Rzepka, Norman Sarkans, Ugis Serrano, Beatriz Tischer, Christian Uhlmann, Virginie Ulman, Vladimír Hartley, Matthew Other Quantitative Biology Image and Video Processing 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. |
| title | MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis |
| topic | Other Quantitative Biology Image and Video Processing |
| url | https://arxiv.org/abs/2311.10443 |