BI‐LAVA: Biocuration With Hierarchical Image Labelling Through Active Learning and Visual Analytics

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dc.contributor.authorTrelles, Juanen_US
dc.contributor.authorWentzel, Andrewen_US
dc.contributor.authorBerrios, Williamen_US
dc.contributor.authorShatkay, Hagiten_US
dc.contributor.authorMarai, G. Elisabetaen_US
dc.date.accessioned2025-03-07T16:48:22Z
dc.date.available2025-03-07T16:48:22Z
dc.date.issued2024
dc.description.abstractIn the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labelled data and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi‐year collaboration with biocurators and text‐mining researchers, we derive an iterative visual analytics and active learning (AL) strategy to address these challenges. We implement this strategy in a system called BI‐LAVA—Biocuration with Hierarchical Image Labelling through Active Learning and Visual Analytics. BI‐LAVA leverages a small set of image labels, a hierarchical set of image classifiers and AL to help model builders deal with incomplete ground‐truth labels, target a hierarchical taxonomy of image modalities and classify a large pool of unlabelled images. BI‐LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections and neighbourhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human–machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labelled and unlabelled collections.en_US
dc.description.number1
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.15261
dc.identifier.issn1467-8659
dc.identifier.pages16
dc.identifier.urihttps://doi.org/10.1111/cgf.15261
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15261
dc.publisherEurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectvisualization
dc.subjectvisual analytics
dc.subjectactive learning
dc.subjectimage labeling
dc.subjectbiomedical images
dc.subject• Human‐centered computing → Visualization → Visualization application domains → Visual analytics • Computing Methodoligies → Machine learning → Learning settings → Active learning settings
dc.titleBI‐LAVA: Biocuration With Hierarchical Image Labelling Through Active Learning and Visual Analyticsen_US
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