A generalized deep learning model for multi-disease Chest X-Ray diagnostics
This addresses the challenge of model generalizability in medical imaging for clinicians, but it is incremental as it builds on existing methods with new data combinations.
The study tackled the problem of generalizing deep learning models for multi-disease chest X-ray diagnostics across different sites, showing that a model trained on multiple datasets performed significantly better for 3 out of 4 disease classes compared to single-dataset models.
We investigate the generalizability of deep convolutional neural network (CNN) on the task of disease classification from chest x-rays collected over multiple sites. We systematically train the model using datasets from three independent sites with different patient populations: National Institute of Health (NIH), Stanford University Medical Centre (CheXpert), and Shifa International Hospital (SIH). We formulate a sequential training approach and demonstrate that the model produces generalized prediction performance using held out test sets from the three sites. Our model generalizes better when trained on multiple datasets, with the CheXpert-Shifa-NET model performing significantly better (p-values < 0.05) than the models trained on individual datasets for 3 out of the 4 distinct disease classes. The code for training the model will be made available open source at: www.github.com/link-to-code at the time of publication.