  A major challenge in applying deep learning to medical imaging is the paucity
of annotated data. This study demonstrates that synthetic colonoscopy images
generated by Generative Adversarial Network (GAN) inversion can be used as
training data to improve the lesion classification performance of deep learning
models. This approach inverts pairs of images with the same label to a
semantically rich & disentangled latent space and manipulates latent
representations to produce new synthetic images with the same label. We perform
image modality translation (style transfer) between white light and narrowband
imaging (NBI). We also generate realistic-looking synthetic lesion images by
interpolating between original training images to increase the variety of
lesion shapes in the training dataset. We show that these approaches outperform
comparative colonoscopy data augmentation techniques without the need to
re-train multiple generative models. This approach also leverages information
from datasets that may not have been designed for the specific colonoscopy
downstream task. E.g. using a bowel prep grading dataset for a polyp
classification task. Our experiments show this approach can perform multiple
colonoscopy data augmentations, which improve the downstream polyp
classification performance over baseline and comparison methods by up to 6%.
