Deep learning for polyp recognition in wireless capsule endoscopy images

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

148 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1379-1389
Journal / PublicationMedical Physics
Volume44
Issue number4
Online published4 Feb 2017
Publication statusPublished - Apr 2017
Externally publishedYes

Abstract

Purpose: Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge arises from the difficulty of robust characterization of images. This study aims to provide discriminative description of WCE images and assist physicians to recognize polyp images automatically.
Methods: We propose a novel deep feature learning method, named stacked sparse autoencoder with image manifold constraint (SSAEIM), to recognize polyps in the WCE images. Our SSAEIM differs from the traditional sparse autoencoder (SAE) by introducing an image manifold constraint, which is constructed by a nearest neighbor graph and represents intrinsic structures of images. The image manifold constraint enforces that images within the same category share similar learned features and images in different categories should be kept far away. Thus, the learned features preserve large intervariances and small intravariances among images.
Results: The average overall recognition accuracy (ORA) of our method for WCE images is 98.00%. The accuracies for polyps, bubbles, turbid images, and clear images are 98.00%, 99.50%, 99.00%, and 95.50%, respectively. Moreover, the comparison results show that our SSAEIM outperforms existing polyp recognition methods with relative higher ORA. 
Conclusion: The comprehensive results have demonstrated that the proposed SSAEIM can provide descriptive characterization for WCE images and recognize polyps in a WCE video accurately. This method could be further utilized in the clinical trials to help physicians from the tedious image reading work.

Research Area(s)

  • image manifold information, polyp recognition, stacked sparse autoencoder with image manifold (SSAEIM), wireless capsule endoscopy images

Citation Format(s)

Deep learning for polyp recognition in wireless capsule endoscopy images. / Yuan, Yixuan; Meng, Max Q.-H.
In: Medical Physics, Vol. 44, No. 4, 04.2017, p. 1379-1389.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review