Guided deep embedded clustering regularization for multifeature medical signal classification - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Pattern Recognition Année : 2023

Guided deep embedded clustering regularization for multifeature medical signal classification

Résumé

Medical signal classification often focuses on one representation (raw signal or time frequency). In that context, recent works have shown the value of exploiting different representations simultaneously. We propose a regularized end-to-end trained model for classification in a medical context exploiting both the raw signal and a time-frequency representation (TFR). First, a 2D convolutional neural network (CNN) encoder and a 1D CNN-transformer encoder start by extracting embedded representations from the TFR and the raw signal, respectively. Then, the obtained embeddings are fused to form a common latent space that is used for classification. We propose to guide the training of each encoder by applying two iterated losses. Moreover, we propose to regularize the fused common latent space using deep embedded clustering. Extensive experiments on three medical datasets and ablation studies show the adaptability and good performance of our method for medical signal classification. Our method makes it possible to improve the classification performance from to MCC on a transcranial Doppler dataset, when compared with single-feature counterparts, while giving more stable models. The code is available at: https://github.com/gdec-submission/gdec/.
Fichier principal
Vignette du fichier
Vindas et al. - 2023 - Guided deep embedded clustering regularization for multifeature medical signal classification.pdf (1.34 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04166454 , version 1 (19-07-2023)

Identifiants

Citer

Yamil Vindas, Emmanuel Roux, Blaise Kévin Guépié, Marilys Almar, Philippe Delachartre. Guided deep embedded clustering regularization for multifeature medical signal classification. Pattern Recognition, 2023, 143, pp.109812. ⟨10.1016/j.patcog.2023.109812⟩. ⟨hal-04166454⟩
52 Consultations
67 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More