  Deep learning semantic segmentation algorithms can localise abnormalities or
opacities from chest radiographs. However, the task of collecting and
annotating training data is expensive and requires expertise which remains a
bottleneck for algorithm performance. We investigate the effect of image
augmentations on reducing the requirement of labelled data in the semantic
segmentation of chest X-rays for pneumonia detection. We train fully
convolutional network models on subsets of different sizes from the total
training data. We apply a different image augmentation while training each
model and compare it to the baseline trained on the entire dataset without
augmentations. We find that rotate and mixup are the best augmentations amongst
rotate, mixup, translate, gamma and horizontal flip, wherein they reduce the
labelled data requirement by 70% while performing comparably to the baseline in
terms of AUC and mean IoU in our experiments.
