You are here

Show:
Printer-friendly versionPrinter-friendly version
  • Liu J, Subramanian K, Yoo TS. Accurate motion parameter estimation for colonoscopy tracking using a regression method. In: Karssemeijer N, Summer RM, editors. Proceedings of SPIE Medical Imaging 2010: Computer-Aided Diagnosis; 2010 Feb 13-18; San Diego;7624:11 pages. DOI: 10.1117/12.844433.
  • Liu J, Subramanian K, Yoo TS. Region flow: a multi-stage method for colonoscopy tracking. Med Image Comput Comput Assist Interv. 2010;13(Pt 2):505-13.
  • Liu J, Subramanian K, Yoo T, Van Uitert R. A Stable Optic-flow Based Method for Tracking Colonoscopy Images. Proceedings of MMBIA 2008, the IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis; 2008 Jun 23-28; Anchorage, Alaska. DOI: 10.1109/CVPRW.2008.4562990.
  • Long LR, Thoma GR. Indexing of Image Content in Spine X-Rays Proc. of SPIE Storage and Retrieval for Image and Video Databases VIII. 2000 Jan;3972:55-63.
  • Long LR, Thoma GR. Use of Shape Models to Search Digitized Spine X-Rays IEEE Computer-Based Medical Systems. 2000 June;: 255-60.
  • Long RL, Antani S, Jeronimo J, Schiffman M, Bopf M, Neve L, Cornwall C, Budihas SC, Thoma GR. Technology for Medical Education, Research, and Disease Screening by Exploitation of Biomarkers in a Large Collection of Uterine Cervix Images Proc CBMS 2006, June 2006, Salt Lake City, Utah; 826-31
  • Long RL, Thoma GR. Segmentation and Image Navigation in Digitized Spine X-Rays Proc. SPIE Medical Imaging: Image Processing. 2000 Feb; 3979: 168-79.
  • Lotenberg S, Gordon S, Long LR, Antani SK, Jeronimo J, Greenspan H. Automatic Evaluation of Uterine Cervix Segmentations Proc. SPIE Medical Imaging 2007. Vol. 6515: 65151J-1-12.
  • Lowekamp B, Rheingans P, Yoo TS. Exploring Surface Characteristics with Interactive Gaussian Images (A Case Study). In: Moorhead R, Gross M, Joy KI, editors. Proceedings of IEEE Visualization 2002 (VIS 2002); 2002 Oct 27-Nov 1; Baltimore, Maryland. Piscataway, New Jersey: IEEE Press. p. 553-6. DOI: 10.1109/VISUAL.2002.1183828.
  • Mann D, Caban JJ, Stolka PJ, Boctor EM, Yoo TS. Multidimensional transfer functions for effective visualization of streaming ultrasound and elasticity images. In: Wong KH, Holmes DR III, editors. Proceedings of SPIE Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling; 2011 Feb 12-17; Lake Buena Vista, Florida;7964:1-8. DOI:10.1117/12.878935.
  • Mao S, Mansukhani P, Thoma GR. Combining Static Classifiers and Class Syntax Models for Logical Entity Recognition in Scanned Historical Documents Proc IEEE CVPR, Minneapolis, Minnesota, June 2007, pp. 1-8.
  • Morris J, Yoo TS, Chen D, Burgess J, Richardson AC. Computer Assisted Pedicle Screw Trajectory Guidance Using Fused Deposition Modeling. Abstract in Computer Assisted Surgery. 2001.
  • Morse B, Yoo TS, Rheingans P, Chen DT, Subramanian KR. Interpolating Implicit Surfaces From Scattered Surface Data Using Compactly Supported Radial Basis Functions. In: Werner R, editor. Proceedings of the SMI 2001 International Conference on Shape Modeling and Applications; 2001 May 7-11; Genova, Italy. Los Alamitos, California: IEEE Computer Society Press; 2001. p. 89-98.
  • Morse BS, Liu W, Yoo TS, Subramanian KR. Active contours using a constraint-based implicit representation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR); 2005 Jun 20-25; San Diego. 285-92. DOI: 10.1109/CVPR.2005.59.
  • 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.
  • Nagy DA, Haidegger T, Yaniv Z. A framework for semi-automatic fiducial localization in volumetric images. In: Linte CA, Yaniv Z, Fallavollita P, Abolmaesumi P, Holmes DR III, editors. Augmented Environments for Computer-Assisted Interventions 2014: Proceedings of 9th International AE-CAI Workshop; 2014 Sep 14; Boston. Lecture Notes in Computer Science vol. 8678. Springer; 2014. p. 138-48.
  • Pearson G, Gill MJ. An Evaluation of Motion JPEG 2000 for Video Archiving. Proc. Archiving 2005. Washington, D.C. April 2005:237-43.
  • Pedrosa G, Rahman MM, Antani SK, Demner-Fushman D, Long LR, Traina A. Integrating visual words as bunch of n-grams for effective biomedical image classification. Proceedings of IEEE WACV 2014: IEEE Winter Conference on Applications of Computer Vision. Steamboat Springs, CO. March 24-26, 2014.
  • Rahman MM, Antani SK, Demner-Fushman D, Thoma GR. A visual concept-based interactive biomedical image retrieval using entropy and spatial information. Proc. SPIE. 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, 94180U. (March 17, 2015) doi: 10.1117/12.2081456.
  • Rahman MM, Antani SK, Thoma GR. Local Concept-Based Medical Image Retrieval With Correlation-Enhanced Similarity Matching Based On Global Analysis IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA10) in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2010. June 2010:87-94
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.

Pages