An evolutionary guided framework for robust latent representation learning in gastrointestinal endoscopy images: EvoLatent-FS

Abstract Learning robust and discriminative representations from gastrointestinal endoscopy images remains challenging due to high visual variability, subtle pathological differences, and pronounced class imbalance. While variational autoencoders (VAEs) have shown promise for unsupervised and semi-supervised representation learning, directly constructing latent spaces from high-dimensional deep features often leads to redundant and clinically irrelevant representations. In this study, we introduce a deep feature learning approach that is called EvoLatent-FS, led by evolution, that directly optimizes feature representations before latent modeling. The suggested method incorporates an intermediary evolutionary optimization stage that refines deep feature representations prior to variational encoding, in contrast to current approaches that either perform feature selection after representation learning or apply latent learning directly to raw deep features. This architecture preserves diagnostically relevant patterns while suppressing superfluous visual input, allowing the learning of more structured and discriminative latent spaces. The three primary parts of the framework are latent representation learning using a variational autoencoder, population-based evolutionary feature optimization via binary feature masking, and deep feature extraction using a convolutional backbone. The suggested method creates a unique representation learning pipeline that is suited to the difficulties of gastrointestinal endoscopic imaging by separating feature optimization from latent modeling and putting it directly before variational encoding. The suggested framework produces more compact latent representations and better downstream classification performance than traditional pipelines that directly apply latent modeling to unoptimized deep features, according to extensive experiments carried out on labeled gastrointestinal endoscopy image datasets. These findings demonstrate the efficacy of evolutionary-guided pre-latent feature optimization as a novel and principled approach to medical image representation learning.