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Figure 1

From: Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia

Figure 1

Distribution of reconstructed clocks in the VAE latent space. (A) Clock drawing reconstructions are represented as a function of the two VAE latent dimensions. This shows the variety of reconstructions generated by the VAE to capture the distribution of the training dataset. (B) Scatterplot showing the distribution of the latent vectors belonging to clocks in the fine-tuning dataset divided into dementia (= 1) and control (= 0) groups. The red curve represents a possible decision boundary between the two groups. Using our neural network classifier, we aim to learn such a decision boundary to classify the two groups.

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