Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation

• URL: http://arxiv.org/abs/2507.15361v1
• Date: Mon, 21 Jul 2025 08:15:17 GMT
• Title: Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation
• Authors: Muhammad Aqeel, Maham Nazir, Zanxi Ruan, Francesco Setti,
• Abstract summary: We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation.<n>Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting.<n>On CVC-ClinicDB, SynDiff achieves 96.4% Dice and 92.9% IoU while maintaining real-time capability suitable for clinical deployment.
• Score: 2.4912767911151015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation suffers from data scarcity, particularly in polyp detection where annotation requires specialized expertise. We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation. Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting, augmenting limited training data with semantically diverse samples. Unlike traditional diffusion methods requiring iterative denoising, we introduce direct latent estimation enabling single-step inference with T x computational speedup. On CVC-ClinicDB, SynDiff achieves 96.0% Dice and 92.9% IoU while maintaining real-time capability suitable for clinical deployment. The framework demonstrates that controlled synthetic augmentation improves segmentation robustness without distribution shift. SynDiff bridges the gap between data-hungry deep learning models and clinical constraints, offering an efficient solution for deployment in resourcelimited medical settings.

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