Nevin Uslu, Merve Çamlibel, Rabiye Erenoğlu
Adaptation of the Child sexual abuse myth scale to Turkish culture: A reliability and validity study
Archives of psychiatric nursing
deep learning dl methods interpretability intrinsically considered part model required better understand relationship clinical imagingbased attributes dl outcomes thus facilitating use reasoning behind medical decisions latent space representations built variational autoencoders vae ensure individual control data attributes attributebased methods enforcing attribute disentanglement proposed literature classical computer vision tasks benchmark data paper propose vae approach attrivae includes attribute regularization term associate clinical medical imaging attributes different regularized dimensions generated latent space enabling betterdisentangled interpretation attributes furthermore generated attention maps explained attribute encoding regularized latent space dimensions using attrivae approach analyzed healthy myocardial infarction patients clinical cardiac morphology radiomics attributes proposed model provided excellent tradeoff reconstruction fidelity disentanglement interpretability outperforming stateoftheart vae approaches according several quantitative metrics resulting latent space allowed generation realistic synthetic data trajectory two distinct input samples along specific attribute dimension better interpret changes different cardiac conditions