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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.
  • Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. In: Suk HI., Liu M., Yan P., Lian C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science, vol 11861. Springer, Cham.
  • 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.
  • Rajaraman S, Candemir S, Xue Z, Alderson P, Thoma G, Antani SK. A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs. In Santosh KC et al. (Eds.). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. (pp. 1-26). New York, NY: CRC Press, Taylor & Francis Group.
  • 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.
  • Ionescu B, Muller H, Peteri R, Dang-Nguyen DT, Piras L, Riegler M, Tran MT, Lux M, Gurrin C, Cid YD, Liauchuk V, Kovalev V, Ben Abacha A, Hasan SA, Datla V, Liu J, Demner-Fushman D, Pelka O, Friedrich CM, Chamberlain J, Clark C, de Herrera AGS, Garcia N, Kavallieratou E, del Blanco CR, Rodriguez CC, Vasillopoulos N, Karampidis K. Multimedia retrieval in medicine, lifelogging, security and nature. International Conference of the Cross-Language Evaluation Forum for European Languages, Springer, Cham, 358-386, 2019
  • 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.

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