Past, present, and future trend of GPU computing in deep learning on medical images

Toto Haryanto, Heru Suhartanto, Xue Lie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A Segmentation process is labeling an image or images for obtaining more meaningfull information. On biomedical images, this activity has an important role in helping pathologist for conducting advance analysis. After Graphical Proceessing Unit (GPU) introduced not only for graphical necessary but also for general purpose computing, segmentation process which is computationally expensive can be potentially improved. The good accuracy of detection and segmentation result provides morphological information for the pathologist. Consequently, more approaches were developed to ensure the good performance of detection and segmentation such as deep learning approach. Convolutional Neural Network (CNN) is one of deep learning architecture with complex computation. This paper presents an overview of utilization of CNN as prominent deep learning architecture under GPU platform and propose an approach of using GPU as potential further parallelie techniques in CNN.

Original languageEnglish
Title of host publication2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-27
Number of pages7
ISBN (Electronic)9781538631720
DOIs
Publication statusPublished - 4 May 2018
Event9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 - Jakarta, Indonesia
Duration: 28 Oct 201729 Oct 2017

Publication series

Name2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Volume2018-January

Conference

Conference9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Country/TerritoryIndonesia
CityJakarta
Period28/10/1729/10/17

Keywords

  • CNN
  • deep learning
  • GPU
  • medical images

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