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

Ke Zhao

cs.AIAug 29, 2026

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

Authors: Ke Zhao

Organizations: Shenyang Institute of Computing Technology, Chinese Academy of Science, Shenyang 110168, China · University of Chinese Academy of Science, Beijing 100049, China

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.

Figures & tables

Fig. 1: The AME module comprises three components: (1) the scalar-valued attributes are transformed into vectors through a mapping function as Eq. 1 . (2) an estimator, which is a two-layer neural network to estimate the parameters $\hat{\mu^{(i)}}$ and $\hat{\sigma^{(i)}}$ of a normal distribution. (3) The latent vector $\hat{z^{(i)}}$ is derived through the reparameterization trick.

Fig. 2: Proposed Framework: (1) The employed COUGHVID dataset [ 13 ] comprises audio signals and annotations for seven attributes. The output of the encoder is a latent vector $z$ . Meanwhile, the attributes undergo transformation via the AME module into a vector $\hat{z}^{\alpha}$ . Both latent vectors, $z^{\beta}$ and $z^{\alpha}$ , are obtained through the reparameterization trick. Alignment between $\hat{z}^{\alpha}$ and $z^{\alpha}$ is achieved by minimizing the $D_{KL}$ .(2) The VAE is trained through a reconstruction task.(3) After pre-training, the normal mean parameters output by the encoder are extracted and utilized as the input for the final disease prediction task.

Fig. 3: The decreasing trend of each part of the loss function in the iteration process. (a) Is the loss function value $L_{vae}$ of VAE, (b) is the value of the loss function $L_{disen}$ part, (c) is the value of the loss function $L_{attri}$ part, (d) is the value of the classification loss $L_{cls}$ .

Models	Precision	Recall	AUC
MobileNetV2	88.42%	84.68%	86.51%
AST	91.32%	82.24%	86.54%
DisenIB	90.12%	91.58%	90.84%
IDB-SR	94.23%	92.43%	93.32%
AGEDR NN({ $z^{\beta},z^{\alpha}$ )}	92.52%	99.00%	95.50%
AGEDR SVM({ $z^{\beta},z^{\alpha}$ })	97.23%	96.65%	97.06%

TABLE I: Classification ability of different models on the validation set.

Models	Precision	Recall	AUC
AGEDR NN({ $z^{\beta},z^{\alpha}$ )}	92.52%	99.00%	95.50%
AGEDR NN( $z^{\beta}$ )	91.45%	75.87%	84.02%

TABLE II: Classification ability of different latent vectors on the validation set.

Panel of Figure 6 — Fig. 4: The t-SNE visualization of $z=\{z^{\beta},z^{\alpha}\}$ under labels and different attributes. Where (a) represents the distribution of $z$ under classification labels, (b) denotes the distribution of $z$ under the attribute ’cough_type’, and (c) shows the distribution of $z$ under the attribute ’severity’.

Disentangled Representation Learning

Disentangled representation learning separates distinct factors of variation so models can interpret, control, and generalize their predictions.

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Medical Image Classification Disentangled Representation Learning Explainable Medical Image Analysis

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Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image classification. Traditional regression-based approaches may ignore non-linear relations between image features and true covariates. To overcome this issue, we present a generalized Medical Imaging Disentanglement Learning (MedIDL) framework. MedIDL maps image features into three mutually orthogonal latent spaces through specialized disentanglement heads: a disease classification head guided by a supervised loss, a covariate-alignment head constrained by cross-subject similarity matching, and a Gaussian head absorbing individual variations. We evaluated our framework across 7 datasets encompassing diverse imaging modalities. MedIDL outperforms state-of-the-art supervised and self-supervised classification methods in accuracy across all datasets. Association analyses demonstrate that MedIDL successfully isolates target-specific latent representations. Gradient-based interpretability mappings localize pathognomonic patterns aligning with established clinical literature.

Abstract

Disentangled Representation Learning

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