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arXiv · 2507.15361

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

Muhammad Aqeel·Maham Nazir·Zanxi Ruan·Francesco Setti
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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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Muhammad Aqeel, Maham Nazir, Zanxi Ruan, Francesco Setti. 2025-07-21. Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation. https://arxiv.org/abs/2507.15361

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