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ANIMO: Annotation of Biomed Image Modalities

Trelles Trabucco, Juan and Li, Pengyuan and Arighi, Cecilia and Raciti, Daniela and Shatkay, Hagit and Marai, G. Elisabeta (2021) ANIMO: Annotation of Biomed Image Modalities. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE , Piscataway, NJ, pp. 1069-1076. ISBN 978-1-6654-0126-5.

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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.

Item Type:Book Section
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URLURL TypeDescription
Trelles Trabucco, Juan0000-0002-0367-9763
Li, Pengyuan0000-0002-8205-7611
Arighi, Cecilia0000-0002-0803-4817
Shatkay, Hagit0000-0001-6953-3970
Additional Information:© 2021 IEEE. This work was supported by awards from the National Institutes of Health (NLM R01LM012527, NLM U01GM120953) and the National Science Foundation (CNS-1828265, CNS-1625941). We thank our collaborators at MGI and GDX at Jackson Labs, in particular Judith Blake and Martin Ringwald, and members of the biocuration research groups at Caltech (WormBase), at the University of Oregon (ZFIN), and at the University of Delaware. We would also like to acknowledge Paul W. Sternberg, WormBase (NIH U24 HG002223).
Funding AgencyGrant Number
NIHU24 HG002223
Record Number:CaltechAUTHORS:20220121-11275000
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Official Citation:J. T. Trabucco, P. Li, C. Arighi, D. Raciti, H. Shatkay and G. E. Marai, "ANIMO: Annotation of Biomed Image Modalities," 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 1069-1076, doi: 10.1109/BIBM52615.2021.9669898
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113044
Deposited By: George Porter
Deposited On:21 Jan 2022 22:48
Last Modified:21 Jan 2022 23:02

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