Latent Diffusion Autoencoders: Toward Efficient and Meaningful
  Unsupervised Representation Learning in Medical Imaging
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
67fd028b27381665ef977a14
Gabriele Lozupone
extracted_confirmed
2025-04-14T12:51:10.674Z
664de98d04ee28f9f944af1c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-HAg0eaZv4PXRfEcBWqhL.png
Gabriele Lozupone
user
gabrielelozupone98
67fd028b27381665ef977a15
Alessandro Bria
67fd028b27381665ef977a16
Francesco Fontanella
67fd028b27381665ef977a17
Frederick J. A. Meijer
67fd028b27381665ef977a18
Claudio De Stefano
67fd028b27381665ef977a19
Henkjan Huisman
Latent Diffusion Autoencoders: Toward Efficient and Meaningful
  Unsupervised Representation Learning in Medical Imaging
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
Latent Diffusion Autoencoder (LDAE) achieves efficient and high-quality unsupervised learning and image generation in medical imaging, particularly for Alzheimer's disease, by operating in a compressed latent space and demonstrating superior performance over conventional diffusion autoencoders.
Latent Diffusion Autoencoder LDAE diffusion-based framework unsupervised learning medical imaging Alzheimer disease brain MR ADNI database diffusion process latent representation linear-probe evaluations ROC-AUC ACC MAE RMSE attribute manipulation semantic interpolation SSIM MSE inference throughput reconstruction quality foundation model