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Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs

  • Yuxing Tang
  • Xiaosong Wang
  • Adam P. Harrison
  • Le Lu
  • Jing Xiao
  • Ronald M. Summers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupled with disease severity-level (DSL) information of a subset. A convolutional neural network (CNN) based attention-guided curriculum learning (AGCL) framework is presented, which leverages the severity-level attributes mined from radiology reports. Images in order of difficulty (grouped by different severity-levels) are fed to CNN to boost the learning gradually. In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration. A two-path network architecture is designed to regress the heatmaps from selected seed samples in addition to the original classification task. The joint learning scheme can improve the classification and localization performance along with more seed samples for the next iteration. We demonstrate the effectiveness of this iterative refinement framework via extensive experimental evaluations on the publicly available ChestXray14 dataset. AGCL achieves over 5.7% (averaged over 14 diseases) increase in classification AUC and 7%/11% increases in Recall/Precision for the localization task compared to the state of the art.

Notes

Acknowledgements

This research was supported by the Intramural Research Program of the National Institutes of Health Clinical Center and by the Ping An Technology Co., Ltd. through a Cooperative Research and Development Agreement. The authors thank NVIDIA for GPU donation.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2018

Authors and Affiliations

  • Yuxing Tang
    • 1
  • Xiaosong Wang
    • 1
  • Adam P. Harrison
    • 1
  • Le Lu
    • 1
  • Jing Xiao
    • 2
  • Ronald M. Summers
    • 1
  1. 1.National Institutes of Health, Clinical CenterBethesdaUSA
  2. 2.Ping An Technology Co., Ltd.ShenzhenChina

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