Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
This work addresses computer-aided diagnosis for thoracic diseases using chest radiographs, providing incremental improvements in modeling label dependencies and handling class imbalance.
The authors tackled multilabel classification of 14 thoracic diseases in chest X-ray images by developing a cascaded deep neural network, achieving better performance than baselines and competitive results with other methods.
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantitative results to answer following research questions for the dataset: 1) What loss functions to use for training DCNN from scratch on ChestX-ray14 dataset that demonstrates high class imbalance and label co occurrence? 2) How to use cascading to model label dependency and to improve accuracy of the deep learning model?