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  • Ionescu B, Müller H, Péteri R, Dang-Nguyen DT, Zhou L, Piras L, Riegler M, Halvorsen P, Tran MT, Lux M, Gurrin C, Chamberlain J, Clark A, Campello A, de Herrera AGS, Ben Abacha A, Datla VV, Hasan SA, Liu J, Demner-Fushman D, Pelka O, Friedrich CM, Cid YD, Kozlovski S, Liauchuk V, Kovalev V, Berari P, Brie P, Fichou D, Dogariu M, Stefan L, Constantin M. ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications. ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications. ECIR (2) 2020: 533-541.
  • Sornapudi S, Brown G, Xue Z, Long LR, Allen L, Antani SK. Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image. AMIA Annu Symp Proc. 2019; 2019: 820–827.
  • Zou J, Xue Z, Brown G, Long LR, Antani SK. Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131811 (2 March 2020); doi: 10.1117/12.2549547
  • Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. To be presented at SPIE Medical Imaging, Feb.18-20, 2020, Houston, USA.
  • Mrabet Y, Demner-Fushman D. On Agreements in Visual Understanding. 2019 Conference on Neural Information Processing Systems. 2019 Conference on Neural Information Processing Systems, December 8-14, 2019. Vancouver, Canada.
  • Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN. Proceedings of AIPR2019, Washington DC, USA, Oct 15-17, 2019.
  • Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019) in conjunction with MICCAI, Shenzhen, China, Oct 13-17, 2019.
  • Demner-Fushman D, Mrabet Y, Ben Abacha A. Consumer health information and question answering: helping consumers find answers to their health-related information needs. JAMIA, 2019.
  • Ganesan P, Rajaraman S, Long LR, Ghoraani B, Antani SK. Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 841 – 844.
  • Ganesan P, Xue Z, Singh S, Long LR, Ghoraani B, Antani SK. Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 4487 – 4490.
  • Rajaraman S, Sornapudi S, Kohli M, Antani SK. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 3689 – 3692.
  • Kim J, Tran L, Chew E, Antani SK. Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning 2019 IEEE 32th International Symposium on Computer-Based Medical Systems (CBMS), pp 489-494, Cordoba, Spain, June 2019.
  • Chowdhuri S, McCrea S, Demner-Fushman D, Overby TC. Extracting Biomedical Terms from Postpartum Depression Online Health Communities. AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:592-601.
  • Zhang XA, Yates A, Vasilevsky N, Gourdine JP, Callahan TJ, Carmody LC, Danis D, Joachimiak MP, Ravanmehr V, Pfaff ER, Champion J, Robasky K, Xu H, Fecho K, Walton NA, Zhu RL, Ramsdill J, Mungall CJ, Kohler S, Haendel MA, McDonald CJ, Vreeman DJ, Peden DB, Bennett TD, Feinstein JA, Martin B, Stefanski AL, Hunter LE, Chute CG, Robinson PN. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ Digit Med. 2019;2. pii: 32. doi: 10.1038/s41746-019-0110-4. Epub 2019 May 2.
  • Guo P, Singh S, Xue Z, Long LR, Antani SK. Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) DOI: 10.1109/BHI.2019.8834495.
  • Kim J, Tran L, Chew E, Antani SK, Thoma GR. Optic Disc Segmentation in Fundus Images Using Deep Learning. SPIE Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, Vol. 10954, San Diego, USA, February 2019.
  • Zolnoori M, Fung K, Patrick DB, Fontelo P, Kharrazi H, Faiola A, Shah ND, Shirley WYS, Eldredge CE, Luo J, Conway M, Zhu J, Park SK, Xu K, Moayyed H. The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications. Data Brief. 2019 Mar 15;24:103838. doi: 10.1016/j.dib.2019.103838. eCollection 2019 Jun.
  • Rajaraman S, Candemir S, Thoma G, Antani SK. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500S (13 March 2019); doi: 10.1117/12.2512752.
  • Candemir S, Rajaraman S, Thoma GR, Antani SK. Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation. Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.
  • Ben Abacha A, Gayen S, Lau JJ, Rajaraman S, Demner-Fushman D. NLM at ImageCLEF 2018 Visual Question Answering in the Medical Domain. CLEF2018 Working Notes. CEUR Workshop Proceedings, Avignon, France, CEUR-WS.org (September 10-14 2018).
  • Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S. Smartphone-Supported Automated Malaria Parasite Detection. SIIM conference on Machine Intelligence in Medical Imaging, 2018.
  • Rajaraman S, Candemir S, Xue Z, Alderson P, Kohli M, Abuya J, Thoma GR, Antani SK. A novel stacked generalization of models for improved TB detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC 2018), Honolulu, Hawaii, 2018. pp. 718-721.
  • Xue Z, Long LR, Jaeger S, Folio L, Thoma GR. Extraction of Aortic Knuckle Contour in Chest Radiographs Using Deep Learning. EMBC 2018.
  • Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR. Gender Detection from Spine X-ray Images Using Deep Learning. Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.
  • Kim J, Candemir S, Chew E, Thoma GR. Region of Interest Detection in Fundus Images Using Deep Learning and Blood Vessel Information. The 31th IEEE International Symposium on Computer-Based Medical Systems. (IEEE CBMS 2018), pp. 357-362, Karlstad, Sweden, June 2018.
  • Vreeman DJ, Abhyankar S, McDonald CJ. Re: Unit conversions between LOINC codes Published June 19, 2017 [Letter]. J Am Med Inform Assoc. 2018 May 1;25(5):614-615. doi: 10.1093/jamia/ocx087.
  • Edinger T, Demner-Fushman D, Cohen AM, Bedrick S, Hersh W. Evaluation of Clinical Text Segmentation to Facilitate Cohort Retrieval. AMIA Annu Symp Proc. 2018 Apr 16;2017:660-669. eCollection 2017.
  • Fung K, Xue Z, Ameye F, Gutierrez AR, D'Have A. Achieving Logical Equivalence between SNOMED CT and ICD-10-PCS Surgical Procedures. AMIA Annu Symp Proc. 2018 Apr 16;2017:724-733. eCollection 2017.
  • Moallem G, Sari-Sarraf H, Poostchi M, Maude RJ, Silamut K, Hossain MA, Antani SK, Jaeger S, Thoma G. Detecting and segmenting overlapping red blood cells in microscopic images of thin blood smears. Proc. SPIE 10581, Medical Imaging 2018:Digital Pathology, 105811F (6 March 2018); doi: 10.1117/12.2293762.
  • Xue Z, Jaeger S, Antani SK, Long LR, Karargyris A, Siegelman J, Folio L, Thoma GR. Localizing tuberculosis in chest radiographs with deep learning. Proc SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790U (6 March 2018) pp. doi: 10.1117/12.2293022
  • Xue Z, Antani SK, Long LR, Thoma GR. Using deep learning for detecting gender in adult chest radiographs. Proc SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790D (6 March 2018) pp. doi: 10.1117/12.2293027.
  • Zohora FT, Antani SK, Santosh KC. Circle-like foreign element detection in chest x-rays using normalized cross-correlation and unsupervised clustering. Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741V (2 March 2018); doi: 10.1117/12.2293739; doi.org/10.1117/12.2293739.
  • Rajaraman S, Antani SK, Candemir S, Xue Z, Abuya J, Kohli M, Alderson P, Thoma GR. Comparing deep learning models for population screening using chest radiography. Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751E (27 February 2018).
  • Xue Z, Jaeger S, Antani SK, Long LR, Karargyris A, Siegelman J, Folio LR, Thoma GR. Localizing tuberculosis in chest radiographs with deep learning. SPIE Medical Imaging 2018
  • Xue Z, Antani SK, Long LR, Thoma GR. Using deep learning for detecting gender in adult chest radiographs. SPIE Medical Imaging 2018
  • 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.
  • Rajaraman S, Antani SK, Xue Z, Candemir S, Jaeger S, Thoma GR. Visualizing abnormalities in chest radiographs through salient network activations in Deep Learning. Proc. IEEE Life Sciences Conference (LSC), Sydney, Australia, 2017. pp. 71-74, DOI:10.1109/LSC.2017.8268146.
  • Abhyankar S, Schluter P, Bennett K, Vreeman D, McDonald CJ. Enabling Interoperability between Healthcare Devices and EHR Systems. In 2017 AMIA Symposium. Wasington DC: IEEE.
  • McDonald CJ, Vreeman D, Wang K, Carr C, Colins B, Abhyankar S, Deckard J, Rubin D, Langlotz C. The LOINC/RSNA Radiology Playbook: A unified terminology for radiology procedures. . In 2017 AMIA Symposium. Washington DC.
  • 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.
  • Kury F, Baik SH, McDonald CJ. Cardioprotective Drugs and Incident Dementias in Medicare's Big Data. AMIA 2017.
  • Fallavollita P, Kersten M, Linte CA, Pratt P, Yaniv Z. Guest Editors' Foreword: Special Issue on Augmented Environments for Computer-Assisted Interventions CAI systems enable more precise, safer, and less invasive interventional treatments [Letter]. Healthc Technol Lett. 2017 Oct 27;4(5):149. doi: 10.1049/htl.2017.0078. eCollection 2017 Oct.
  • Ekong DU, Fontelo P. Prototype telepathology solutions that use the Raspberry Pi and mobile devices. 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose CA, 2017: 1-4.
  • Fung K, Gutierrez A, Ameye F, D’Have A, Ariel B. Demonstrating the Benefits of Mapping SNOMED CT to ICD-10-PCS through a Prototype Application for End User Implementation. SNOMED Expo Oct 2017, Bratislava, Slovakia pp. 0
  • Guan Y, Li M, Jaeger S, Lure F, Raptopoulos V, Lu P, Folio LR, Candemir S, Antani SK, Siegelman J, Li J, Wu T, Thoma GR, Qu S. Applying Artificial Intelligence and Radiomics for Computer Aided Diagnosis and Risk Assessment in Chest Radiographs. 2nd Conference on Machine Intelligence in Medical Imaging (CMIMI) of the Society for Imaging Informatics in Medicine (SIIM), Poster, 2017.
  • Abhyankar S, Vreeman DJ, Westra BL, Delaney CW. Comments on the Use of LOINC and SNOMED CT for Representing Nursing Data [Letter]. Int J Nurs Knowl. 2017 Aug 30. doi: 10.1111/2047-3095.12183. [Epub ahead of print]
  • Raje S, Bodenreider O. Interoperability of disease concepts in clinical and research ontologies – Contrasting coverage and structure in the Disease Ontology and SNOMED CT. Stud Health Technol Inform. 2017;245:925-929.
  • Rajaraman S, Antani SK, Jaeger S. Visualizing Deep Learning Activations for Improved Malaria Cell Classification. Proceedings of The First Workshop in Medical Informatics and Healthcare (MIH 2017), Proceedings of Machine Learning Research (PMLR), v. 69, p. 40-47.
  • 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

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