Abstract
Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) according to the target class distribution. Although DA using generative adversarial network (GAN) has the potential to overcome the disadvantages of conventional DA, there are not enough cases where this technique has been applied to medical images, and in particular, not enough cases where quantitative evaluation was used to determine whether the generated images had enough realism and diversity to be used for DA. In this study, we synthesized 18F-Florbetaben (FBB) images using CGAN. The generated images were evaluated using various measures, and we presented the state of the images and the similarity value of quantitative measurement that can be expected to successfully augment data from generated images for DA. The method includes (1) conditional WGAN-GP to learn the axial image distribution extracted from pre-processed 3D FBB images, (2) pre-trained DenseNet121 and model-agnostic metrics for visual and quantitative measurements of generated image distribution, and (3) a machine learning model for observing improvement in generalization performance by generated dataset. The Visual Turing test showed similarity in the descriptions of typical patterns of amyloid deposition for each of the generated images. However, differences in similarity and classification performance per axial level were observed, which did not agree with the visual evaluation. Experimental results demonstrated that quantitative measurements were able to detect the similarity between two distributions and observe mode collapse better than the Visual Turing test and t-SNE.
Highlights
50 million people worldwide have dementia, and nearly 10 million new cases occur each year
The metrics selected to measure a similarity of a given data distribution in this experiment used recently reported model-agnostic metrics [30,33] including Maximum mean discrepancy (MMD), Fréchet inception distance (FID), and The 1-nearest neighbor classifier (1-neural network (NN)) leave-one-out (LOO)
We used quantitative measurements to examine the generalization dataset used in this study
Summary
50 million people worldwide have dementia, and nearly 10 million new cases occur each year. This number is expected to increase to 82 million by 2030 and 152 million by. Alzheimer’s disease (AD), which is present in 70% of patients with dementia, is the most prevalent dementia-causing illness. It degrades memory and, thinking skills and eventually renders a person unable to maintain an independent life [3]. Positron emission tomography (PET) is an ultrasensitive and non-invasive molecular imaging technique used to detect functional activity within organs that are expected to be
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