  Chest x-rays are the most common radiology studies for diagnosing lung and
heart disease. Hence, a system for automated pre-reporting of pathologic
findings on chest x-rays would greatly enhance radiologists' productivity. To
this end, we investigate a deep-learning framework with novel training schemes
for classification of different thoracic pathology labels from chest x-rays. We
use the currently largest publicly available annotated dataset ChestX-ray14 of
112,120 chest radiographs of 30,805 patients. Each image was annotated with
either a 'NoFinding' class, or one or more of 14 thoracic pathology labels.
Subjects can have multiple pathologies, resulting in a multi-class, multi-label
problem. We encoded labels as binary vectors using k-hot encoding. We study the
ResNet34 architecture, pre-trained on ImageNet, where two key modifications
were incorporated into the training framework: (1) Stochastic gradient descent
with momentum and with restarts using cosine annealing, (2) Variable image
sizes for fine-tuning to prevent overfitting. Additionally, we use a heuristic
algorithm to select a good learning rate. Learning with restarts was used to
avoid local minima. Area Under receiver operating characteristics Curve (AUC)
was used to quantitatively evaluate diagnostic quality. Our results are
comparable to, or outperform the best results of current state-of-the-art
methods with AUCs as follows: Atelectasis:0.81, Cardiomegaly:0.91,
Consolidation:0.81, Edema:0.92, Effusion:0.89, Emphysema: 0.92, Fibrosis:0.81,
Hernia:0.84, Infiltration:0.73, Mass:0.85, Nodule:0.76, Pleural
Thickening:0.81, Pneumonia:0.77, Pneumothorax:0.89 and NoFinding:0.79. Our
results suggest that, in addition to using sophisticated network architectures,
a good learning rate, scheduler and a robust optimizer can boost performance.
