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

2026-08-28

AI Summary

该论文提出了一种基于属性高斯估计的解缠表示学习框架AGEDR，通过最小化互信息来提高医学声音诊断中深度学习模型的公平性和可解释性。

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.

Problem

• fairness
• interpretability
• medical sound diagnosis

Innovation

• Disentangled Representation Learning
• Attributes-based Gaussian Estimation
• Variational AutoEncoder (VAE)
• Attribute Mapping Embedding (AME)