Acquisition of interpretable domain information during brain MR image harmonization for content-based image retrieval

Medical images like MR scans often show domain shifts across imaging sites due to scanner and protocol differences, which degrade machine learning performance in tasks such as disease classification. Domain harmonization is thus a critical research focus. Recent approaches encode brain images boldsymbolx into a low-dimensional latent space boldsymbolz, then disentangle it into boldsymbolzu (domain-invariant) and boldsymbolzd (domain-specific), achieving strong results. However, these methods often lack interpretability-an essential requirement in medical applications-leaving practical issues unresolved. We propose Pseudo-Linear-Style Encoder Adversarial Domain Adaptation (PL-SE-ADA), a general framework for domain harmonization and interpretable representation learning that preserves disease-relevant information in brain MR images. PL-SE-ADA includes two encoders fE and fSE to extract boldsymbolzu and boldsymbolzd, a decoder to reconstruct the image fD, and a domain predictor gD. Beyond adversarial training between the encoder and domain predictor, the model learns to reconstruct the input image boldsymbolx by summing reconstructions from boldsymbolzu and boldsymbolzd, ensuring both harmonization and informativeness. Compared to prior methods, PL-SE-ADA achieves equal or better performance in image reconstruction, disease classification, and domain recognition. It also enables visualization of both domain-independent brain features and domain-specific components, offering high interpretability across the entire framework.