For small retinal OCT datasets, lower FID between augmentation and training data predicts larger segmentation improvements, but the effect is non-monotonic and differs between synthetic and standard augmentations.
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
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abstract
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.
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Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation
For small retinal OCT datasets, lower FID between augmentation and training data predicts larger segmentation improvements, but the effect is non-monotonic and differs between synthetic and standard augmentations.