Multimodal Deep Learning for Cervical Dysplasia Diagnosis

  • Tao Xu
  • Han Zhang
  • Xiaolei Huang
  • Shaoting Zhang
  • Dimitris N. Metaxas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

To improve the diagnostic accuracy of cervical dysplasia, it is important to fuse multimodal information collected during a patient’s screening visit. However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal information. We first employ the convolutional neural network (CNN) to convert the low-level image data into a feature vector fusible with other non-image modalities. We then jointly learn the non-linear correlations among all modalities in a deep neural network. Our multimodal framework is an end-to-end deep network which can learn better complementary features from the image and non-image modalities. It automatically gives the final diagnosis for cervical dysplasia with 87.83 % sensitivity at 90 % specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tao Xu
    • 1
  • Han Zhang
    • 2
  • Xiaolei Huang
    • 1
  • Shaoting Zhang
    • 3
  • Dimitris N. Metaxas
    • 2
  1. 1.Computer Science and Engineering DepartmentLehigh UniversityBethlehemUSA
  2. 2.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  3. 3.Department of Computer ScienceUNC CharlotteCharlotteUSA

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