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  • Xue Z, Antani S, Long LR, Jeronimo J, Thoma GR. Investigating CBIR Techniques for Cervicographic Images AMIA Annu Symp Proc. 2007 Oct 11:826-30.
  • 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.
  • Yang F, Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Automated Parasite Classification of Malaria on Thick Blood Smears. ASTMH 67th Annual Meeting, New Orleans, LA, Oct. 28 – Nov. 1, 2018.
  • 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.
  • Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani SK. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2019 Sep 23. doi: 10.1109/JBHI.2019.2939121.
  • 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.
  • Yang S, Guo J, King PS, Sriraja Y, Long LR. Multispectral Digital Cervigram Analyser in the Wavelet Domain for Early Detection of Cervical Cancer Proc. SPIE Medical Imaging. 2004 Feb; 5370.
  • Yaniv Z, Lowekamp B, Johnson HJ, Beare R. SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research. J Digit Imaging. 2018 Jun;31(3):290-303. doi: 10.1007/s10278-017-0037-8.
  • Yaniv Z, Faruque J, Howe S, Dunn K, Sharlip D, Bond A, Perillan P, Bodenreider O, Ackerman M, Yoo TS. The national library of medicine pill image recognition challenge: An initial report. Z. Yaniv et al., "The national library of medicine pill image recognition challenge: An initial report," 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, 2016, pp. 1-9. doi: 10.1109/AIPR.2016.8010584.
  • Yaniv Z. Registration for Orthopaedic Interventions. Chapter 3 in: Zheng G, Li S, editors. Computational Radiology for Orthopaedic Interventions: Lecture Notes in Computational Vision and Biomechanics. Springer International Publishing; c2016. p. 41-70.
  • Yaniv Z, Linte CA. Applications of Augmented Reality in the Operating Room. Chapter 19 in Fundamentals of Wearable Computers and Augmented Reality, 2nd ed. CRC Press, 2015.
  • Yaniv Z, Holmes DR III, editors. Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling. Proceedings of conference held 2014 Feb 15-20; San Diego. Proc. SPIE 9036; 2014 Mar 12. 764 pages. ISBN: 9780819498298.
  • Yao J, Antani S, Long LR, Thoma GR, Zhang Z. Automatic Medical Image Annotation and Retrieval Using SECC Proc CBMS 2006, June 2006, Salt Lake City, Utah; 105-10
  • Yoo T, Ackerman MJ, Lorensen W, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R. Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK - The Insight Toolkit. In: Westwood JD, Hoffman HM, Robb RA, Stredney D, editors. Stud Health Technol Inform [Studies in Health Technology and Informatics] -- Proceedings of the 10th annual Medicine Meets Virtual Reality conference – Digital Upgrades: Applying Moore’s Law to Health; 2002 Jan 23-26; Newport Beach, California;85:586-92. Amsterdam: IOS Press.
  • Yoo TS, Bliss D, Lowekamp B, Chen D, Murphy GE, Narayan K, Hartnell LM, Do T, Subramaniam S. Visualizing cells and humans in 3D: Biomedical image analysis at nanometer and meter scales. IEEE Computer Graphics and Applications. 2012 Sep-Oct;32(5):39-49. DOI: 10.1109/MCG.2012.68.
  • Yoo TS, Hamilton T, Hurt D, Caban J, Liao D, Chen D. Toward Quantitative X-Ray CT Phantoms of Metastatic Tumors Using Rapid Prototyping Technology. In: Pan X, Liebling M, editors. Proceedings of ISBI 2011: IEEE Computer Society International Symposium on Biomedical Imaging: From Nano to Macro; 2011 Mar 30-Apr 2; Chicago. p. 1770-3. DOI: 10.1109/ISBI.2011.5872749.
  • Yoo TS, Silver D, Correa C, Chen D, Moran A. Volumetric Bodies - the Exhibition. IEEE VisWeek 2009 Interactive Demo & Art Exhibit; 2009 Oct 12-16; Atlantic City, New Jersey.
  • Yoo TS, Rheingans P, Chen D, Olano M, Lowekamp B. Animated embroidery: a teapot in modern blackwork. ACM SIGGRAPH 2006 Teapot Exhibit entry; 2006 Jul 30-Aug 3; Boston.
  • Yoo TS. 3D Medical Informatics: Information Science in Multiple Dimensions. In: Chen H, Fuller SS, Friedman C, Hersh W, editors. Medical Informatics: Knowledge Management and Data Mining in Biomedicine. New York: Springer Science. p. 333-58.
  • Yoo TS. The Insight Toolkit: An Open-Source Initiative in Data Segmentation and Registration. In: Johnson C, Hansen C, editors. The Visualization Handbook. Amsterdam: Elsevier; 2005. p. 733-48.
  • Yoo TS, Metaxas DN. Open science – combining open data and open source software: Medical image analysis with the Insight Toolkit. Med Image Anal. 2005 Dec;9(6):503-6. Epub 2005 Sep 19.
  • Yoo TS, Ackerman MJ. Open Source Software for Medical Image Processing and Visualization. Communications of the ACM. 2005 Feb;48(2):55-9. DOI:10.1145/1042091.1042120.
  • Yoo TS, editor. Insight Into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. Natick, Massachusetts: A.K. Peters; 2004 Aug 16. 410 pages.