Medical image classification with convolutional neural network

Qing Li, Weidong Cai, Xiaogang Wang, Yun Zhou, David Dagan Feng, Mei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

Original languageEnglish (US)
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages844-848
Number of pages5
ISBN (Print)9781479951994
DOIs
StatePublished - Mar 19 1997
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: Dec 10 2014Dec 12 2014

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period12/10/1412/12/14

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Artificial Intelligence
  • Control and Systems Engineering

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