Publication details
MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Nature Methods |
| MU Faculty or unit | |
| Citation | |
| web | https://www.nature.com/articles/s41592-025-02835-8.pdf?utm_source=clarivate&getft_integrator=clarivate |
| Doi | https://doi.org/10.1038/s41592-025-02835-8 |
| Keywords | DEEP MICROSCOPY; SEGMENTATION; CELL; EMBRYOS |
| Description | 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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