You are here

Open-i

Screen detail of OpenI Web page
Project information

The Open-ism (pronounced “open eye”) experimental multimedia search engine retrieves and displays structured MEDLINE citations augmented by image-related text and concepts and linked to images based on image features.

The Open-i project provides novel information retrieval services for biomedical articles from the full text collections such as PubMed Central. It is unique in its ability to index both the text and images in the articles. The article retrieval is powered by the LHNCBC-built search engine Essie.

The Open-I biomedical image search engine lets users retrieve not only the MEDLINE citation information, but also the outcome statements in the article and the most relevant figure from it. Further, it is possible to use the figure as a query component to find other relevant images or other visually similar images. Future stages aim to provide image region-of-interest (ROI) based querying. The initial number of images is projected to be around 600,000 and will scale to millions. The extensive image analysis and indexing and deep text analysis and indexing require distributed computing. Future plans include making the image computation services available as a NLM service.

Publications/Tools: 
Ben Abacha A, De Herrera A, Gayen S, Demner-Fushman D, Antani SK. NLM at ImageCLEF 2017 Caption Task. International Conference of the Cross-Language Evaluation Forum for European Languages 2017 Sep 11 (pp. 358-360). Springer, Cham.
Candemir S, Antani SK, Xue Z, Thoma GR. Novel Method for Storyboarding Biomedical Videos for Medical Informatics. 30th IEEE International Symposium on Computer-Based Medical Systems
Xue Z, Antani SK, Long LR, Thoma GR. Automatic multi-label annotation of abdominal CT images using CBIR. Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 1013807 (March 13, 2017); doi:10.1117/12.2254368.
Chachra S, Ben Abacha A, Shooshan SE, Rodriguez L, Demner-Fushman D. A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers: 1093-1100.
De Herrera A, Long LR, Antani SK. Content-Based fMRI Brain Maps Retrieval. International Conference on Brain and Health Informatics, Omaha, NE, USA, October 13-16, 2016.
De Herrera A, Schaer R, Antani SK, Müller H. Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation. In: Carneiro G. et al. (eds) Deep Learning and Data Labeling for Medical Applications. LABELS 2016, DLMIA 2016. Lecture Notes in Computer Science, vol 10008. Springer, Cham
Narum R, Zou J, Antani SK. Semi-Automated Ground-Truth Data Collection and Annotation for Journal Figure Analysis [Poster]. 2016 NIH Research Festival
Xue Z, Rahman M, Antani SK, Long LR, Demner-Fushman D, Thoma GR. Modality Classification for Searching Figures in Biomedical Literature. Proceedings of the IEEE 29th International Symposium on Computer-Based Medical Systems, pp. 152-157, 2016. doi:10.1109/CBMS.2016.29.
Demner-Fushman D, Antani SK, Chachra S, Kushnir M, Gayen S. Open-i Imaging informatics, natural language processing and multi-modal information retrieval - research and development Technical Report to the LHNCBC Board of Scientific Counselors September 2016
Ben Abacha A, Demner-Fushman D. Meta-Learning with Selective Data Augmentation for Medical Entity Recognition. International Journal of Computational Linguistics and Applications (IJCLA) 7(2): 167-182 (2016)

Pages