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Imaging Tools for Cancer Research

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.

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 Pathology Informatics 2016, 7:51 (30 December 2016) DOI:10.4103/2153-3539.197193
Roberts K, Simpson M, Demner-Fushman D, Voorhees E, Hersh W. State-of-the-art in biomedical literature retrieval for clinical cases: a survey of the TREC 2014 CDS track. Information Retrieval Journal. 2016, 19 (1-2); 113-148.
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.
KC S, Vajda S, Antani S, Thoma GR. Edge map analysis in chest X-rays for automatic pulmonary abnormality screening. Int J Comput Assist Radiol Surg. 2016 Sep;11(9):1637-46. doi: 10.1007/s11548-016-1359-6. Epub 2016 Mar 19.
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.
Mrabet Y, Kilicoglu H, Demner-Fushman D. Unsupervised Ranking of Knowledge Bases for Named Entity Recognition. 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, Holland, Aug 29 - Sep 02, 2016.
Candemir S, Jaeger S, Antani S, Bagci U, Folio LR, Xu Z, Thoma G. Atlas-based rib-bone detection in chest X-rays. Comput Med Imaging Graph. 2016 Jul;51:32-9. doi: 10.1016/j.compmedimag.2016.04.002. Epub 2016 Apr 13.
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
Xu T, Xin C, Long LR, Antani SK, Xue Z, Kim E, Huang X. A New Image Data Set and Benchmark for Cervical Dysplasia Classification Evaluation. Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, LNCS 9352, pp. 26–35, 2015. DOI: 10.1007/978-3-319-24888-2 4.
Rahman MM, Antani SK, Demner-Fushman D, Thoma GR. Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval. J Med Imaging (Bellingham). 2015 Oct;2(4):046502. doi: 10.1117/1.JMI.2.4.046502. Epub 2015 Dec 30.