Hi, I'm Anuar Aimoldin

Background

Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model.

Findings

Unassisted radiologists had a macroaveraged AUC of 0.713 across the 127 clinical findings, compared with 0.808 when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deep-learning model.

Interpretation

This study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice.

This work presents a comparative experimental analysis of different first-order stochastic optimization algorithms for image registration in spatial domain: stochastic gradient descent, Momentum, Nesterov momentum, Adagrad, RMSprop, Adam. Correlation coefficient is considered as the objective function. Experiments are performed on synthetic data generated via wave model with different noise-to-signal ratio and real-world images.

Key Findings

The comparative analysis shows that in each case "classical" stochastic gradient descent shows the worst result in terms of the convergence rate. The best results are provided by Adam and RMSprop optimizations, with Adam algorithm being almost always preferable as it has less variance than RMSprop.