Extending Pretrained Diffusion Models for Medical Image-Label Generation
Abstract
Medical image segmentation often suffers from the limitation of small-scale annotated datasets. To address this challenge, we propose Paired Diffusion Models (PDM), a framework that adapts large-scale pre-trained diffusion models (e.g., Stable Diffusion) to generate paired image-label data. By modifying the neural network’s architecture to accept and output both images and their pixel-level annotations simultaneously, PDM enables a single model to generate an image and its corresponding binary segmentation mask. Furthermore, we introduce a mask decoder that directly decodes binary masks from the latent space, bypassing the need for an intermediate RGB-to-binary conversion step. By carefully initializing additional parameters, PDM stabilizes training and enhances the accuracy of the generated segmentation masks. Experimental results demonstrate that pretraining with images generated by PDM significantly boosts the performance of medical image segmentation models, especially in scenarios with limited annotated data.
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