CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Nov 1, 2019

Abstract

OBJECTIVE: This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance.

Authors

  • Li Tong
    Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332.
  • Hang Wu
    Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
  • May D Wang
    Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332.

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