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Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector

  • Sumedha Singla
  • Mingming Gong
  • Siamak Ravanbakhsh
  • Frank Sciurba
  • Barnabas Poczos
  • Kayhan N. BatmanghelichEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11070)

Abstract

We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning that requires a fixed dimensional input, our method operates on a set of image patches; hence it can accommodate variable length input image without image resizing. The model learns a clinically interpretable subject-level representation that is reflective of the disease severity. Our model consists of three mutually dependent modules which regulate each other: (1) a discriminative network that learns a fixed-length representation from local features and maps them to disease severity; (2) an attention mechanism that provides interpretability by focusing on the areas of the anatomy that contribute the most to the prediction task; and (3) a generative network that encourages the diversity of the local latent features. The generative term ensures that the attention weights are non-degenerate while maintaining the relevance of the local regions to the disease severity. We train our model end-to-end in the context of a large-scale lung CT study of Chronic Obstructive Pulmonary Disease (COPD). Our model gives state-of-the art performance in predicting clinical measures of severity for COPD. The distribution of the attention provides the regional relevance of lung tissue to the clinical measurements.

Notes

Acknowledgement

This work is partially supported by NIH Award Number 1R01HL141813-01. We gratefully thank NVIDIA Corporation for their donation of the Titan X Pascal GPU. We thank Competitive Medical Research Fund (CMRF) grant for their funding.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sumedha Singla
    • 1
  • Mingming Gong
    • 2
  • Siamak Ravanbakhsh
    • 3
  • Frank Sciurba
    • 4
  • Barnabas Poczos
    • 5
  • Kayhan N. Batmanghelich
    • 1
    • 2
    • 5
    Email author
  1. 1.Computer Science DepartmentUniversity of PittsburghPittsburghUSA
  2. 2.Department of Biomedical InformaticsUniversity of PittsburghPittsburghUSA
  3. 3.Computer Science DepartmentUniversity of British ColumbiaVancouverCanada
  4. 4.University of Pittsburgh School of MedicineUniversity of PittsburghPittsburghUSA
  5. 5.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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