HadoopBase-MIP: Hadoop & HBase-based Toolkit for medical image processing
This page contains information related to Shunxing Bao’s poster, “HadoopBase-MIP: Hadoop & HBase-based Toolkit for medical image processing”, at Supercomputing 2019.
Poster
Presentation
View Presentation Slides (PDF)
Related papers
- Bao, S., Plassard, A.J., Landman, B.A. and Gokhale, A., 2017, April. Cloud engineering principles and technology enablers for medical image processing-as-a-service. In Cloud Engineering (IC2E), 2017 IEEE International Conference on (pp. 127-137). IEEE.
- Bao, S., Weitendorf, F.D., Plassard, A.J., Huo, Y., Gokhale, A. and Landman, B.A., 2017, February. Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine. In Proceedings of SPIE–the International Society for Optical Engineering (Vol. 10138). NIH Public Access.
- Bao, Shunxing, Yuankai Huo, Prasanna Parvathaneni, Andrew J. Plassard, Camilo Bermudez, Yuang Yao, Ilwoo Lyu, Aniruddha Gokhale, and Bennett A. Landman. “A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design.” In Proceedings of SPIE–the International Society for Optical Engineering, vol. 10597. NIH Public Access, 2018.
- Shunxing Bao, Prasanna Parvathaneni, Yuankai Huo, Yogesh Barve, Andrew J. Plassard, Yuang Yao, Hongyang Sun, Ilwoo Lyu, David H. Zald, Bennett A. Landman and Aniruddha Gokhale. ” Technology Enablers for Cloud-based Multi-level Analysis Applications in Medical Image Processing ” IEEE BigData 2018, Seattle. (accepted). Available upon request; contact us.