Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis

Researchers proposed AGEDR, a disentangled representation learning framework that uses Attributes-based Gaussian Estimation and Attribute Mapping Embedding modules to improve fairness and interpretability in medical sound diagnosis, outperforming conventional classification models and existing disentangled representation learning methods in experiments. The source code is publicly available on GitHub.

arXiv:2608.29026v1 Announce Type: new Abstract: Deep learning has a powerful capability of feature extraction. However, the lack of fairness and interpretability in deep neural networks poses limitations to their adoption in the medical domain. This paper proposes a disentangled representation learning DisenRL framework, named the Attributes-based Gaussian Estimation for Disentangled Representation AGEDR , which incorporates Attribute Mapping Embedding AME modules designed to map attributes into vectors and align them with a subset of the latent vectors in a Variational AutoEncoder VAE . This part of the latent vector will be disentangled from the remaining latent vectors by minimizing mutual information. A classifier is then trained using the mean parameters of the latent vectors from the VAE. Extensive experiments demonstrate that AGEDR outperforms both conventional classification models and existing disentangled representation learning methods. The ablation experiments also indicate the disentangling capability and fairness of AGEDR. The source code is publicly available at https://github.com/ZhaoKe1024/DisentangledRepr.