Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis
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The paper introduces MedIDL, a Medical Imaging Disentanglement Learning framework that separates disease-related features from confounding covariates and individual variability in medical images. It achieves this by projecting image features into three orthogonal latent spaces—disease classification, covariate alignment, and a Gaussian head for individual variation—using specialized disentanglement heads. Across seven diverse imaging datasets, MedIDL surpasses state‑of‑the‑art supervised and self‑supervised methods in classification accuracy, and its latent representations and gradient‑based visualizations align with known clinical patterns.
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The paper introduces a deep generative model using conditional variational autoencoders to augment vital sign data from healthy individuals so that it mimics patterns of specific clinical conditions. Trained on a publicly available ICU dataset, the model learns the underlying dynamics of ICU data and reshapes healthy data to align with target clinical labels. A proposed distance metric demonstrates that the generated samples are more aligned with intended clinical labels than baseline methods.
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