标题： SkinDualGen：皮肤病变中同时图像-掩码生成的提示驱动扩散模型 显示英文标题

标题： SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions

摘要： 医学图像分析在诸如皮肤病变等疾病的早期诊断中起着关键作用。 然而，数据的稀缺性和类别不平衡显著阻碍了深度学习模型的性能。 我们提出了一种新方法，利用预训练的Stable Diffusion-2.0模型生成高质量的合成皮肤病变图像和相应的分割掩码。 这种方法增强了分类和分割任务的训练数据集。 我们通过领域特定的低秩适应（LoRA）微调和多目标损失函数的联合优化来适应Stable Diffusion-2.0，使模型能够在单一步骤中同时生成基于文本描述的临床相关图像和分割掩码。 实验结果表明，生成的图像经过FID分数验证，其质量与真实图像非常接近。 结合真实和合成数据的混合数据集显著提高了分类和分割模型的性能，在准确率和F1分数上实现了8%到15%的显著提升，并在其他关键指标如Dice系数和IoU上也获得了额外的积极增长。 我们的方法提供了一个可扩展的解决方案，以应对医学成像数据的挑战，有助于提高罕见疾病诊断的准确性与可靠性。 显示英文摘要

摘要： Medical image analysis plays a pivotal role in the early diagnosis of diseases such as skin lesions. However, the scarcity of data and the class imbalance significantly hinder the performance of deep learning models. We propose a novel method that leverages the pretrained Stable Diffusion-2.0 model to generate high-quality synthetic skin lesion images and corresponding segmentation masks. This approach augments training datasets for classification and segmentation tasks. We adapt Stable Diffusion-2.0 through domain-specific Low-Rank Adaptation (LoRA) fine-tuning and joint optimization of multi-objective loss functions, enabling the model to simultaneously generate clinically relevant images and segmentation masks conditioned on textual descriptions in a single step. Experimental results show that the generated images, validated by FID scores, closely resemble real images in quality. A hybrid dataset combining real and synthetic data markedly enhances the performance of classification and segmentation models, achieving substantial improvements in accuracy and F1-score of 8% to 15%, with additional positive gains in other key metrics such as the Dice coefficient and IoU. Our approach offers a scalable solution to address the challenges of medical imaging data, contributing to improved accuracy and reliability in diagnosing rare diseases.