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Imaging Tools for Cancer Research

Screenshot of the Boundary Marking Tool created for cancer research.
Project information
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The goal of our work in Biomedical Imaging is two-fold: One, to develop advanced imaging tools for biomedical research in partnership with the National Cancer Institute and other organizations. Secondly, to conduct research in Content Based Image Retrieval (CBIR) to index and retrieve medical images by image features (e.g., shape, color and texture), augmented by textual features as well. This work includes the development of the CervigramFinder for retrieval of uterine cervix images by image features, SPIRS for retrieval of digitized x-ray images of the spine from NHANES II and a distributed global system SPIRS-IRMA for image retrieval by both high-level and detailed features of medical images, in collaboration with Aachen University, Germany.

CBIR is also an aspect of the Image Text Indexing (ITI) project that seeks to automatically index illustrations in medical articles by processing text in figure captions and mentions in the article, as well as image features in the illustrations.

Publications/Tools: 
Almubarak H, Guo P, Stanley RJ, Long LR, Antani SK, Thoma GR. Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images. in Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics,. IGI Global (Hershey, PA).
Bryant B, Sari-Sarraf H, Long LR, Antani SK. A Kernel Support Vector Machine Trained Using Approximate Global and Exhaustive Local Sampling. Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) 2017, Austin, Texas, USA, December 2017. Pp. 267-8 DOI: https://doi.org/10.1145/3148055.3149206
de Herrera G, Long LR, Antani SK. Graph Representation for Content–based fMRI Activation Map Retrieval. Proceedings of 1st Life Sciences Conference, Sydney, Australia, December 2017 pp. 129-32 DOI: https://doi.org/10.1109/LSC.2017.8268160.
Almubarak HA, Stanley RJ, Long LR, Antani SK, Thoma GR, Zuna R, Frazier SR. Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images. Procedia Computer Science, Volume 114, 2017, Pages 281-287, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.09.044.
KC S, Aafaque A, Antani SK, Thoma GR. Line Segment-Based Stitched Multipanel Figure Separation for Effective Biomedical CBIR. Int. J. Patt. Recogn. Artif. Intelligence 31, 1757003 (2017) https://doi.org/10.1142/S0218001417570038.
Guo P, Almubarak H, Banerjee K, Stanley RJ, Long LR, Antani SK, Thoma GR, Zuna R, Frazier S, Moss R, Stoecker W. Enhancements in localized classification for uterine cervical cancer digital histology image assessment. J Pathol Inform. 2016 Dec 30;7:51. doi: 10.4103/2153-3539.197193. eCollection 2016.
Ben Abacha A, de Herrera A, Wang Ke, Long LR, Antani SK, Demner-Fushman D. Named entity recognition in functional neuroimaging literature. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, 2017, pp. 2218-2220.
Mrabet Y, Vougiouklis P, Kilicoglu H, Gardent C, Demner-Fushman D, Hare J, Simperl E. Aligning Texts and Knowledge Bases with Semantic Sentence Simplification. WebNLG 2016.
Xu T, Zhang H, Xin C, Kim E, Long LR, Xue Z, Antani SK, Huang Z. Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation. Pattern Recognition. ISSN 0031-3203, DOI: 10.1016/j.patcog.2016.09.027.
Guo P, Banerjee K, Stanley RJ, Long LR, Antani SK, Thoma GR, Frazier SR, Moss RH, Stoecker WV. Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification. DOI 10.1109/JBHI.2015.2483318 IEEE Journal of Biomedical and Health Informatics

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