We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.
@article{arxiv.1807.01257,
title = {A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities},
author = {Bo Zhou and Yuemeng Li and Jiangcong Wang},
journal= {arXiv preprint arXiv:1807.01257},
year = {2018}
}
Comments
10 pages, 6 figures; accepted by IEEE Winter Conference on Applications of Computer Vision (2019 WACV)