Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder

Ayantika Das; Keerthi Ram; Mohanasankar Sivaprakasam

cs.CVMar 2, 2026

Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder

Authors: Ayantika Das, Keerthi Ram, Mohanasankar Sivaprakasam

Organizations: Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India · Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India

Abstract

Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progression, essential for the assessment of diseases like Alzheimer's. Among the existing generative approaches, recent diffusion-based models have emerged as an effective alternative to generate disease progression images. Incorporating multi-modal and non-imaging attributes as conditional information into diffusion frameworks has been shown to improve controllability during such generations. However, existing methods do not explicitly ensure that information from non-imaging conditioning modalities is meaningfully aligned with image features to introduce desirable changes in the generated images, such as modulation of progression-specific regions. Further, more precise control over the generation process can be achieved by introducing progression-relevant structure into the internal representations of the model, lacking in the existing approaches. To address these limitations, we propose a diffusion autoencoder-based framework for disease progression modeling that explicitly enforces alignment between different modalities. The alignment is enforced by introducing an explicit objective function that enables the model to focus on the regions exhibiting progression-related changes. Further, we devise a mechanism to better structure the latent representational space of the diffusion auto-encoding framework. Specifically, we assign separate latent subspaces for integrating progression-related conditions and retaining subject-specific identity information, allowing better-controlled image generation. These results demonstrate that enforcing alignment and better structuring of the latent representational space of diffusion auto-encoding framework leads to more anatomically precise modeling of Alzheimer's disease progression.

Figures & tables

Fig. 1: Flow diagram indicating the desirable information to be modeled in the latent representational space of our approach.

Fig. 2: Left to right: The condition encoder ( $\mathcal{C}$ ) integrates progression information with the latent representation of baseline image ( $x_{b}$ ) produced by the encoding component ( $\mathcal{E}$ ), guiding the denoising decoder ( $\mathcal{D}$ ) to generate follow-up image ( $\hat{x}_{f}$ ). Cross-attention ( $A_{l}$ ) is computed between the conditioning vector and decoder layers, and the objective functions enforce alignment of $A_{l}$ with the progression-specific mask ( $M$ ).

Methods	PSNR (dB) ( $\uparrow$ )	SSIM ( $\uparrow$ )	MSE ( $\downarrow$ )
	CN/ MCI & AD	CN/ MCI & AD	CN/ MCI & AD
Naive Baseline	27.25 $\pm$ 2.12/ 26.75 $\pm$ 2.07	0.93 $\pm$ 0.021/ 0.92 $\pm$ 0.021	0.0021 $\pm$ 0.001/ 0.0024 $\pm$ 0.001
IPGAN [ 24 ]	25.86 $\pm$ 2.12/ 25.31 $\pm$ 2.13	0.92 $\pm$ 0.032/ 0.91 $\pm$ 0.034	0.0030 $\pm$ 0.001/ 0.0034 $\pm$ 0.002
BrLP [ 17 ]	26.71 $\pm$ 1.02/ 26.20 $\pm$ 1.14	0.79 $\pm$ 0.022/ 0.79 $\pm$ 0.025	0.0029 $\pm$ 0.001/ 0.0030 $\pm$ 0.001
DE-CVAE [ 4 ]	27.32 $\pm$ 2.98/ 26.99 $\pm$ 2.83	0.65 $\pm$ 0.090/ 0.63 $\pm$ 0.082	0.0023 $\pm$ 0.001/ 0.0024 $\pm$ 0.001
SITGAN [ 23 ]	28.73 $\pm$ 3.25/ 28.09 $\pm$ 3.23	0.94 $\pm$ 0.033/ 0.93 $\pm$ 0.034	0.0019 $\pm$ 0.001/ 0.0022 $\pm$ 0.001

TABLE I: Quantitative evaluations of Align-cDAE with the baseline methods in terms of image-level metrics (PSNR, SSIM, MSE). Statistical significance (

p<0.01

) is marked with ( ∗ ).

Fig. 3: Left to right: In sub-figures (A) and (B), the columns (a)-(f) present a method-wise comparison of predicted follow-up images ( $\hat{x}_{f}$ ) along with their error maps computed with respect to the ground-truth follow-up ( $|\hat{x}_{f}-x_{f}|$ ) and ground truth baseline ( $|x_{b}-\hat{x}_{f}|$ ), respectively. Column (g) in sub-figure (A) shows the ground-truth follow-up ( ${x}_{f}$ ), while in sub-figure (B) it also shows the ground-truth follow-up and baseline difference ( $|x_{b}-x_{f}|$ ). Top to bottom: For both sub-figures, the upper and lower two rows correspond to subjects aged 83.3 and 84.3 years, respectively, from the AD disease category. In sub-figure (A), the green box highlights that Align-cDAE better predicts progression-related changes.

Methods	MAE ( $\downarrow$ )
	Hippocampus	Amygdala	Lateral Ventricles
IPGAN [ 24 ]	0.3348 $\pm$ 0.0338	0.3283 $\pm$ 0.0260	0.5445 $\pm$ 0.5240
BrLP [ 17 ]	0.1960 $\pm$ 0.0552	0.1731 $\pm$ 0.0529	0.3702 $\pm$ 0.1012
DE - CVAE [ 4 ]	0.1871 $\pm$ 0.0727	0.1183 $\pm$ 0.1117	0.1747 $\pm$ 0.1470
SITGAN [ 23 ]	0.1161 $\pm$ 0.0288	0.0211 $\pm$ 0.0201	0.1436 $\pm$ 0.1053
cDAE	0.0550 $\pm$ 0.0500	0.0200 $\pm$ 0.0400	0.1250 $\pm$ 0.1200

TABLE II: Volumetric assessment of Align-cDAE with baseline methods in terms of MAE in different anatomical regions. Statistical significance (

p<0.01

) is marked with ( ∗ ).

Fig. 4: Left to right: (a) Ground truth, (b) cDAE, and (c) Align-cDAE. Top to bottom: (i) Ground-truth and predicted follow-up images, (ii) (a) Ground truth baseline and (b)-(c) model attention maps overlaid on images, and (iii) Difference between ground-truth/ predicted follow-up and ground-truth baseline images.

Data Mixture	Accuracy (%)
$100\%$ RD $+0\%$ GD	$81.92$
$75\%$ RD $+25\%$ GD	$82.02$
$50\%$ RD $+50\%$ GD	$82.15$
$25\%$ RD $+75\%$ GD	$82.00$
$0\%$ RD $+100\%$ GD	$81.93$

TABLE III: Cognitive state classification accuracy under different mixtures of real baseline data (RD) and generated follow-up data (GD).

Methods	PSNR( $\uparrow$ ) (dB)	SSIM( $\uparrow$ )	FID( $\downarrow$ )	Accuracy( $\uparrow$ ) (%)
	CN/ MCI & AD			Classify CN vs. AD
Align-cDAE (w/o L_attn_imax)	28.58/ 28.54	0.945/ 0.938	7.56/ 7.88	80.99
Align-cDAE	29.82/ 29.52	0.950/ 0.944	6.85/ 6.93	81.93

TABLE IV: Ablation study comparing the image similarity and diversity metrics, along with downstream classification performance.

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Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, India · Sudha Gopalakrishnan Brain Centre (SGBC), IITM, Chennai 600036, India
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AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders

Ayantika Das, Arunima Sarkar, Keerthi Ram, Mohanasankar Sivaprakasam

Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, India · Sudha Gopalakrishnan Brain Centre (SGBC), IITM, Chennai 600036, India

Medical Image Synthesis Alzheimer's Disease Progression Prediction Diffusion Autoencoder Conditional Generative Modeling Diffusion Models

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Generative modeling frameworks have emerged as an effective approach to capture high-dimensional image distributions from large datasets without requiring domain-specific knowledge, a capability essential for disease progression modeling. Recent generative approaches have attempted to capture progression by mapping images to a latent space and guiding representations to generate follow-up images from previous time points. However, these methods impose constraints on distribution learning, resulting in latent spaces with limited controllability for generating follow-up images without paired subject-specific longitudinal guidance. In order to enable controlled movements in the latent representational space and generate progression images from a previous time-point image without subject-specific guidance, we introduce a conditionable Diffusion Auto-encoder framework that forms a compact latent space capturing high-level semantics and providing means to control generation. Our approach leverages this latent space to condition and apply controlled shifts to the representations of previous time-point images by isolating progression and subject identity information for generating follow-up images. The shifts are implicitly guided by correlating with progression attributes and constraining to Alzheimer's disease specific regions, without paired longitudinal guidance. We validate the generations through image quality metrics, volumetric progression analysis, and downstream tasks in Alzheimer's disease datasets from different sources. This demonstrates the effectiveness of our approach for Alzheimer's progression modeling and longitudinal image generation.

Abstract

Diffusion Autoencoder

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