2025/12/17 by Dvoretskii, Stefan, Archit, Anwai, Pape, Constantin +2
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Scientific Computing and Data Management #Single-cell and spatial transcriptomics #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2512.15820
openalex publication_date 2025/12/17 · openalex created_date 2025/12/21 · openalex updated_date 2026/07/28
Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging datasets must be accompanied with suitable, high-quality annotations and metadata. Although established data repositories such as the Image Data Resource (IDR) and BioImage Archive offer rich metadata, their contents typically cannot be directly consumed by image analysis tools without substantial data wrangling. Such a tedious assembly and conversion of (meta)data can account for a dedicated amount of time investment for researchers, hindering the development of more powerful analysis tools. Here, we introduce BioimageAIpub, a workflow that streamlines bioimaging data conversion, enabling a seamless upload to HuggingFace, a widely used platform for sharing machine learning datasets and models.