  Heart failure remains a major public health challenge with growing costs.
Ejection fraction (EF) is a key metric for the diagnosis and management of
heart failure however estimation of EF using echocardiography remains expensive
for the healthcare system and subject to intra/inter operator variability.
While chest x-rays (CXR) are quick, inexpensive, and require less expertise,
they do not provide sufficient information to the human eye to estimate EF.
This work explores the efficacy of computer vision techniques to predict
reduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMIC
CXR-jpg (MCR) dataset. Our work establishes benchmarks using multiple
state-of-the-art convolutional neural network architectures. The subsequent
analysis shows increasing model sizes from 8M to 23M parameters improved
classification performance without overfitting the dataset. We further show how
data augmentation techniques such as CXR rotation and random cropping further
improves model performance another ~5%. Finally, we conduct an error analysis
using saliency maps and Grad-CAMs to better understand the failure modes of
convolutional models on this task.
