Figures within biomedical articles present essential evidence of the relevance of a publication in a curation workflow. In particular, visual cues of the image modality or experimental methods can help expert curators identify relevant papers from an increasing number of publications. Automating the identification of these content-bearing images can thus be helpful in computer-assisted curation. However, the paucity of labeled datasets and the specialized training required to label such images hinder the development of such tools. To address this problem, we present the design of ANIMO, a labeling system that integrates extraction and segmentation tools to ease the annotation burden. We first introduce two taxonomies of image modalities and experimental methods, derived in collaboration with curators. On the back-end of the system, we process batches of documents and create a labeling task per document. At the front-end, expert curators can access these tasks through a web interface and access the article of interest. We describe the evaluation of this system by a group of biocurators, and the human factor lessons learned from this interdisciplinary experience.

doi: 10.1109/BIBM52615.2021.9669898

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Trelles Trabucco, J., Li, P., Arighi, C., Raciti, D., Shatkay, H., Marai, G.E., ANIMO: Annotation of Biomed Image Modalities, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1069-1076, December 9th, 2021. https://ieeexplore.ieee.org/document/9669898