Advances in Parallel Computing and Databases for Digital Pathology in Cancer Research

Abstract : AbstractOver the past decade there have been significantadvances in bringing parallel computing and new databasemanagement systems to a wider audience. Through a numberof efforts such as the National Strategic Computing Initiative(NSCI), there has been a push to merge these Big Data andScientific Computing communities to a single computationalplatform. At the Massachusetts Institute of Technology, LincolnLaboratory, we have been developing HPC and database technologiesto address a number of scientific problems includingbiomedical processing. In this article, we briefly describe thesetechnologies and how we have used them in the past. Weare interested in learning more about the needs of clinicalpathologists as we continue to develop these technologies.

[1]  Lars George,et al.  HBase: The Definitive Guide , 2011 .

[2]  S. Swerdlow WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues , 2017 .

[3]  Jeremy Kepner,et al.  'pMATLAB Parallel MATLAB Library' , 2007, Int. J. High Perform. Comput. Appl..

[4]  Vijay Gadepally,et al.  MATLAB for Signal Processing on Multiprocessors and Multicores , 2010, IEEE Signal Processing Magazine.

[5]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[6]  Linda G. Shapiro,et al.  Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study , 2016, Journal of Digital Imaging.

[7]  A. Krishnamurthy,et al.  Developing a Computational Science IDE for HPC Systems , 2007, Third International Workshop on Software Engineering for High Performance Computing Applications (SE-HPC '07).

[8]  Jeremy Kepner,et al.  D4M: Bringing associative arrays to database engines , 2015, 2015 IEEE High Performance Extreme Computing Conference (HPEC).

[9]  Metin Nafi Gürcan,et al.  Detection of Follicles From IHC-Stained Slides of Follicular Lymphoma Using Iterative Watershed , 2010, IEEE Transactions on Biomedical Engineering.

[10]  Michael Stonebraker,et al.  The design of POSTGRES , 1986, SIGMOD '86.

[11]  Anil K. Jain,et al.  Prostate cancer grading: Gland segmentation and structural features , 2012, Pattern Recognit. Lett..

[12]  Henrik Loeser,et al.  "One Size Fits All": An Idea Whose Time Has Come and Gone? , 2011, BTW.

[13]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[14]  Michael Stonebraker,et al.  A Demonstration of the BigDAWG Polystore System , 2015, Proc. VLDB Endow..

[15]  Metin Nafi Gürcan,et al.  An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry , 2012, J. Comput. Sci..

[16]  Nikita Shamgunov The MemSQL In-Memory Database System , 2014, IMDM@VLDB.

[17]  S. Samsi,et al.  Glomeruli segmentation in H&E stained tissue using perceptual organization , 2012, 2012 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).