Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction

• URL: http://arxiv.org/abs/2506.05428v1
• Date: Thu, 05 Jun 2025 07:01:05 GMT
• Title: Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction
• Authors: Zhihao Tang, Chaozhuo Li, Litian Zhang, Xi Zhang,
• Abstract summary: We propose a diffusion-based framework that synthesizes clinically plausible future sMRI representations directly from baseline data.<n>Experiments on ADNI and AIBL cohorts show that MCI-Diff outperforms state-of-the-art baselines.
• Score: 13.937881108738042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early prediction of Mild Cognitive Impairment (MCI) conversion is hampered by a trade-off between immediacy--making fast predictions from a single baseline sMRI--and accuracy--leveraging longitudinal scans to capture disease progression. We propose MCI-Diff, a diffusion-based framework that synthesizes clinically plausible future sMRI representations directly from baseline data, achieving both real-time risk assessment and high predictive performance. First, a multi-task sequence reconstruction strategy trains a shared denoising network on interpolation and extrapolation tasks to handle irregular follow-up sampling and learn robust latent trajectories. Second, an LLM-driven "linguistic compass" is introduced for clinical plausibility sampling: generated feature candidates are quantized, tokenized, and scored by a fine-tuned language model conditioned on expected structural biomarkers, guiding autoregressive generation toward realistic disease patterns. Experiments on ADNI and AIBL cohorts show that MCI-Diff outperforms state-of-the-art baselines, improving early conversion accuracy by 5-12%.

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