Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

TL;DR

This paper introduces a cascaded deep neural network approach for multilabel classification of 14 thoracic diseases in chest X-rays, demonstrating improved accuracy over baseline models on the ChestX-ray14 dataset.

Contribution

It proposes a novel cascaded deep learning model that effectively models label dependencies and handles class imbalance for multi-disease diagnosis in chest radiographs.

Findings

Outperforms baseline models on ChestX-ray14 dataset

Effective handling of class imbalance and label co-occurrence

Cascaded model improves diagnostic accuracy

Abstract

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?