  In 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 labeled 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 strategy to address these challenges. We implement this strategy in a
system called BI-LAVA Biocuration with Hierarchical Image Labeling through
Active Learning and Visual Analysis. BI-LAVA leverages a small set of image
labels, a hierarchical set of image classifiers, and active learning to help
model builders deal with incomplete ground-truth labels, target a hierarchical
taxonomy of image modalities, and classify a large pool of unlabeled images.
BI-LAVA's front end uses custom encodings to represent data distributions,
taxonomies, image projections, and neighborhoods 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 labeled and unlabeled
collections.
