E-mail: firstname.lastname@example.orgSchool/College: Seidenberg School of Computer Science and Information Systems
Office Hours: (Spring 2016)
Computer ScienceBS, Harbin Institute of Technology, Harbin, China, 2004
Shan, J., Alam, K., Garra, B., Zhang, Y. & Ahmed, T. (2015). The Development of Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BIRADS Features and Machine Learning Methods. Ultrasound in Medicine and Biology (UMB).
Shan, J. (2014, December). A Novel Multiplayer Tracking System for Short Track Speed Skating. IET Computer Vision. , pages 16. http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2014.0001
Shan, J. (2014, May). A New Scheme to Evaluate the Accuracy of Knowledge Representation in Automated Breast Cancer Diagnosis. http://cts2014.cisedu.info/
Shan, J. (2012). Breast Ultrasound Image Segmentation Based on Neutrosophic L-means Clustering.
Shan, J. (2012, September). “Effective and Automatic Breast Ultrasound Image Segmentation Using L-Means Clustering. Medical Physics. Vol 39 (Issue 9) , pages 5669-5682.
Shan, J. & , . (2012, February). Completely Automated Segmentation Approach for Breast Ultrasound Images Using Multiple-Domain Features. Ultrasound Med Biol.. Vol 38 (Issue 2)
Shan, J. (2014, May 21). The 2014 International Conference on Collaboration Technologies and Systems (CTS 2014). A Similarity Measurement of Clinical Trials Using SNOMED - A Preliminary Study. IEEE, ACM, IFIP, Minneapolis, MN
Shan, J. (2014, May 20). The 2014 International Conference on Collaboration Technologies and Systems (CTS 2014). A New Scheme to Evaluate the Accuracy of Knowledge Representation in Automated Breast Cancer Diagnosis. IEEE, ACM, IFIP, Minneapolis, MN
(2013, October 05).
Pace DPS seminar.
Telehealth and computer-aided diagnosis for breast cancer.
Seidenberg School of CSIS, Graduate Center