  The identification and localisation of pathological tissues in medical images
continues to command much attention among deep learning practitioners. When
trained on abundant datasets, deep neural networks can match or exceed human
performance. However, the scarcity of annotated data complicates the training
of these models. Data augmentation techniques can compensate for a lack of
training samples. However, many commonly used augmentation methods can fail to
provide meaningful samples during model fitting. We present local gamma
augmentation, a technique for introducing new instances of intensities in
pathological tissues. We leverage local gamma augmentation to compensate for a
bias in intensities corresponding to ischemic stroke lesions in human brain
MRIs. On three datasets, we show how local gamma augmentation can improve the
image-level sensitivity of a deep neural network tasked with ischemic lesion
segmentation on magnetic resonance images.
