Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Journal:
arXiv
Published Date:
Apr 11, 2025
Abstract
This study presents Latent Diffusion Autoencoder (LDAE), a novel
encoder-decoder diffusion-based framework for efficient and meaningful
unsupervised learning in medical imaging, focusing on Alzheimer disease (AD)
using brain MR from the ADNI database as a case study. Unlike conventional
diffusion autoencoders operating in image space, LDAE applies the diffusion
process in a compressed latent representation, improving computational
efficiency and making 3D medical imaging representation learning tractable. To
validate the proposed approach, we explore two key hypotheses: (i) LDAE
effectively captures meaningful semantic representations on 3D brain MR
associated with AD and ageing, and (ii) LDAE achieves high-quality image
generation and reconstruction while being computationally efficient.
Experimental results support both hypotheses: (i) linear-probe evaluations
demonstrate promising diagnostic performance for AD (ROC-AUC: 90%, ACC: 84%)
and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic
representations enable attribute manipulation, yielding anatomically plausible
modifications; (iii) semantic interpolation experiments show strong
reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month
gap. Even for longer gaps (24 months), the model maintains robust performance
(SSIM > 0.93, MSE < 0.004), indicating an ability to capture temporal
progression trends; (iv) compared to conventional diffusion autoencoders, LDAE
significantly increases inference throughput (20x faster) while also enhancing
reconstruction quality. These findings position LDAE as a promising framework
for scalable medical imaging applications, with the potential to serve as a
foundation model for medical image analysis. Code available at
https://github.com/GabrieleLozupone/LDAE
