Titan rtx 24 g

Since only 16% of the dataset contained exact Cobb angle (572/3587), it was challenging to derive regression-based prediction using supervised learning models. To overcome this, we proposed a semi-supervised classifier using StyleGAN2-ADA (13 ). StyleGAN2-ADA is a generative model that can create high-quality images with only about thousands of images by adding augmentation to the discriminator. This generative model forms a multivariate distribution by learning the distribution of images, which is a latent space. Fig. 1 shows a diagram of our proposed method. Our algorithm performs the Scoliosis classification task in the third step through two preceding steps, upstream task and projection: 1) upstream task training (a preceding training step for the downstream task): training StyleGAN2-ADA to extract rich semantics from data distributions, 2) projection: project downstream task training and validation images onto latent space to extract vectors where semantic representations were embedded, and 3) downstream task training and testing (a final step for scoliosis classification of our proposed model): train and evaluate a simple classifier comprising a multi-layer perceptron (MLP) (14 ) using projected vectors. Pytorch 1.8 was used for the deep learning framework, and single NVIDIA Titan RTX 24G was used for GPU computing.