Network Pruning for OCT Image Classification

Bing Yang, Yi Zhang, Jun Cheng, Jin Zhang, Lei Mou, Huaying Hao, Yitian Zhao, Jiang Liu

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

Abstract

Convolutional neural network (CNN) has expanded rapidly, and has been widely used in medical image classification. The large number of parameters in a neural network makes CNN models computationally expensive. This leads to slow inference speed, especially for 3D data such as optical coherence tomography (OCT) for retinal images. A volume OCT scan of retina often contains hundreds of 2D images which needs to be analyzed sequentially in a local computer with limited computational resources. We introduce network pruning to OCT images classification and propose an algorithm to prune networks. We compress the popular classification models, such as ResNet and VGG. For example, within 1% accuracy loss, we compress ResNet-18 from 44.8 MB to 69 KB and VGG-16 from 537.1 MB to 194 KB. These pruned models are much smaller and easier to deploy on the OCT devices. As for the inference speed, the pruned models are 10 to 20 times faster than original models for ResNet and VGG in CPU.

Original languageEnglish
Title of host publicationOphthalmic Medical Image Analysis - 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer
Pages121-129
Number of pages9
ISBN (Print)9783030329556
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11855 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period17/10/1917/10/19

Keywords

  • Classification
  • Network pruning
  • OCT

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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