  BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the
last years have ground-truth labels intended for different computer vision
tasks, suggesting that performance in automated chest-xray interpretation might
improve by using a method that can exploit diverse types of annotations. This
work presents a Deep Learning method based on the late fusion of different
convolutional architectures, that allows training with heterogeneous data with
a simple implementation, and evaluates its performance on independent test
data. We focused on obtaining a clinically useful tool that could be
successfully integrated into a hospital workflow. MATERIALS AND METHODS: Based
on expert opinion, we selected four target chest x-ray findings, namely lung
opacities, fractures, pneumothorax and pleural effusion. For each finding we
defined the most adequate type of ground-truth label, and built four training
datasets combining images from public chest x-ray datasets and our
institutional archive. We trained four different Deep Learning architectures
and combined their outputs with a late fusion strategy, obtaining a unified
tool. Performance was measured on two test datasets: an external
openly-available dataset, and a retrospective institutional dataset, to
estimate performance on local population. RESULTS: The external and local test
sets had 4376 and 1064 images, respectively, for which the model showed an area
under the Receiver Operating Characteristics curve of 0.75 (95%CI: 0.74-0.76)
and 0.87 (95%CI: 0.86-0.89) in the detection of abnormal chest x-rays. For the
local population, a sensitivity of 86% (95%CI: 84-90), and a specificity of 88%
(95%CI: 86-90) were obtained, with no significant differences between
demographic subgroups. We present examples of heatmaps to show the accomplished
level of interpretability, examining true and false positives.
