Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation

• URL: http://arxiv.org/abs/2505.06068v1
• Date: Fri, 09 May 2025 14:07:27 GMT
• Title: Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
• Authors: Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, Mingjie Sun, Yongxin Guo,
• Abstract summary: We introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion.<n>During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion.
• Score: 9.795456238314825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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