457 research outputs found
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
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.Comment: To be presented at IEEE International Symposium on Biomedical Imaging
(ISBI), 201
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
One of the biggest issues facing the use of machine learning in medical
imaging is the lack of availability of large, labelled datasets. The annotation
of medical images is not only expensive and time consuming but also highly
dependent on the availability of expert observers. The limited amount of
training data can inhibit the performance of supervised machine learning
algorithms which often need very large quantities of data on which to train to
avoid overfitting. So far, much effort has been directed at extracting as much
information as possible from what data is available. Generative Adversarial
Networks (GANs) offer a novel way to unlock additional information from a
dataset by generating synthetic samples with the appearance of real images.
This paper demonstrates the feasibility of introducing GAN derived synthetic
data to the training datasets in two brain segmentation tasks, leading to
improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage
points under different conditions, with the strongest effects seen fewer than
ten training image stacks are available
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