A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

Abstract

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.

arXiv comment10 pages, 6 figures; accepted by IEEE Winter Conference on Applications of Computer Vision (2019 WACV)

References in corpus (4)

• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Learning to diagnose from scratch by exploiting dependencies among labels
• Dilated Residual Networks
• Extracting Lungs from CT Images using Fully Convolutional Networks

Cited by in corpus (5)

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• Convolutional Nets for Diabetic Retinopathy Screening in Bangladeshi Patients
• Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays
• Contralaterally Enhanced Networks for Thoracic Disease Detection
• A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions