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Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis — arXiv:2608.29026

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29026
License (as archived): CC0-1.0
Archived: 2026-10-17T07:43:59.210Z
SHA-256 of the archived text: 5a7d4d8292739e6bf6d03b4366010adb69ef10b503a8f21844482ca3ff8e78d0
Stable URL of this page: https://metraipolis.com/documento/dc_5157287c2ec9

How to cite

Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis — arXiv:2608.29026 — arXiv (en), https://arxiv.org/abs/2608.29026 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5157287c2ec9 (Metraipolis archive dc_5157287c2ec9, sha256 5a7d4d8292739e6bf6d03b4366010adb69ef10b503a8f21844482ca3ff8e78d0)

BibTeX

@misc{dc5157287c2ec9,
  title = {Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis — arXiv:2608.29026},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29026},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5157287c2ec9; sha256 5a7d4d8292739e6bf6d03b4366010adb69ef10b503a8f21844482ca3ff8e78d0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29026. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV); Audio and Speech Processing (eess.AS).
Pagina del lavoro: https://arxiv.org/abs/2608.29026 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:28:26.501Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).