A Vector Quantized Autoencoder Model for the Interpretable Classification of Hepatic Ultrasound Textures - Mello, Fabrizio P.; Machado, Alexei M. C. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21417, 2026)
Texture analysis in liver ultrasound is central to steatosis assessment, as pathological alterations are primarily reflected in microtextural patterns. This work proposes a vector quantized variational autoencoder (VQ-VAE) for faithful reconstruction and interpretable latent representation of hepatic textures. Experiments were conducted on 550 image patches of the liver, normalized by the hepatorenal index, where the reconstruction quality and discriminative capacity of the latent space were evaluated. The model based on VQ-VAE architecture achieved the best overall performance among the fully reconstructed autoencoder models with respect to Mean Squared Error, Structural Similarity Index and Peak Signal-to-Noise Ratio. Moreover, a Support Vector Machine (SVM) trained on 32-dimensional latent vectors achieved the same accuracy as a SVM trained on raw pixels while reducing dimensionality from 784 to 32 features. These results demonstrate that VQ-VAE preserves microtextures, organizes the latent space in a structured manner, and produces compact, discriminative representations, highlighting its potential for quantitative and interpretable liver ultrasound analysis.