title: Latent Diffusion Autoencoders: Toward Efficient and Meaningful
    Unsupervised Representation Learning in Medical Imaging
summary: 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
paper_authors._id: 67fd028b27381665ef977a14
paper_authors.name: Gabriele Lozupone
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-14T12:51:10.674Z
paper_authors._id: 664de98d04ee28f9f944af1c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-HAg0eaZv4PXRfEcBWqhL.png
paper_authors.fullname: Gabriele Lozupone
paper_authors.type: user
paper_authors.user: gabrielelozupone98
paper_authors._id: 67fd028b27381665ef977a15
paper_authors.name: Alessandro Bria
paper_authors._id: 67fd028b27381665ef977a16
paper_authors.name: Francesco Fontanella
paper_authors._id: 67fd028b27381665ef977a17
paper_authors.name: Frederick J. A. Meijer
paper_authors._id: 67fd028b27381665ef977a18
paper_authors.name: Claudio De Stefano
paper_authors._id: 67fd028b27381665ef977a19
paper_authors.name: Henkjan Huisman
paper_title: Latent Diffusion Autoencoders: Toward Efficient and Meaningful
    Unsupervised Representation Learning in Medical Imaging
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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