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Distributed Deep Learning for Multi-Label Chest Radiography Classification

Chest radiography supports the clinical diagnosis and treatment for a series of thoracic diseases, such as cardiomegaly, pneumonia, and lung lesion. With the revolution of deep learning and the availability of large chest radiography datasets, binary chest radiography classifiers have been widely proposed in the literature. However, these automatic classifiers neglect label co-occurrence and inter-dependency in chest radiography and fail to make full use of accelerators, resulting in inefficient and computationally expensive models. This paper first studies the effect of chest radiography image format, variations of Dense Convolutional Network (DenseNet-121) architecture, and parallel training on chest radiography multi-label classification task. Then, we propose Xclassifier, an efficient multi-label classifier that trains an enhanced DenseNet-121 with a blur pooling framework to classify chest radiography based on fourteen predefined labels. Xclassifier accomplishes an ideal memory utilization and GPU computation and achieves 84.10% AUC on the MIMIC-CXR dataset and 83.89% AUC on the CheXpert dataset. The code used to generate the experiment results mentioned in this paper can be found here: https://github.com/MaramMonshi/Xclassifier.