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.

BioVisionCenter Universität Zürich Zürich Switzerland

Cancer Early Detection Advanced Research Knight Cancer Institute Oregon Health and Science University Portland OR USA

Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick MD USA

CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany

Center for Molecular Microscopy Center for Cancer Research National Cancer Institute NIH Bethesda MD USA

Collaboration for joint PhD degree between EMBL and Heidelberg University Faculty of Biosciences Heidelberg Germany

Department of Experimental Medical Science Lund University Bioimaging Centre and Nanolund Lund University Lund Sweden

Division of Computational Genomics and System Genetics German Cancer Research Center Heidelberg Germany

Edinburgh Pathology Centre for Genomic and Experimental Medicine and CRUK Scotland Centre Institute of Genetics and Cancer University of Edinburgh Edinburgh UK

Electron Microscopy Science Technology Platform Francis Crick Institute London UK

Euro BioImaging ERIC Bio Hub European Molecular Biology Laboratory Heidelberg Heidelberg Germany

European Molecular Biology Laboratory Data Science Heidelberg Germany

European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK

European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany

Fondazione Human Technopole 5 le Rita Levi Montalcini Milan Italy

German BioImaging Gesellschaft für Mikroskopie und Bildanalyse e 5 Konstanz Germany

Imaging Platform Broad Institute Cambridge MA USA

Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain

IT4Innovations VSB Technical University of Ostrava Ostrava Poruba Czech Republic

Nantes Université CHU Nantes CNRS Inserm BioCore US16 SFR Bonamy Nantes France

Scalable Minds GmbH Potsdam Germany

Universidad Carlos 3 de Madrid Madrid Spain

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