用噪声一致性设计新模型，提升医学图像合成质量。
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
- 双分支结构同步训练，通过噪声一致性损失增强形态保真度。
- 在肠镜和皮肤癌数据集上，分割指标提升1.5%至4.4%。
- 适合需要高质量合成数据的医学图像分割研究者。
深度学习已革新医学图像分割，但其潜力受限于标注数据稀缺。尽管扩散模型可生成合成图像-掩码对以扩充数据集，却仍面临相同的数据匮乏问题。传统仅基于掩码的模型常生成低保真图像，难以捕捉解剖形态细节，影响分割模型的鲁棒性。为此，我们提出Siamese-Diffusion，一种包含掩码扩散与图像扩散的双组件模型。训练时引入两组件间的噪声一致性损失，以提升掩码扩散在参数空间中的形态保真度；推理时仅使用掩码扩散，保障多样性与可扩展性。大量实验表明该方法优势显著：在Polyps数据集上，SANet的mDice与mIoU分别提升3.6%和4.4%；在ISIC2018上，UNet分别提升1.52%和1.64%。代码已开源。
原文摘要 · Abstract (English)
Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018. Code is available at GitHub.
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