用门控条件扩散模型精准生成乳腺影像与病灶，提升数据多样性与真实感。
Joint Holistic and Lesion Controllable Mammogram Synthesis via Gated Conditional Diffusion Model
- 基于潜空间去噪扩散框架，融合软掩码嵌入实现解剖结构一致性
- 通过门控分支动态选择病灶特征，实现对小病灶的精确控制
- 适合医学图像合成、数据增强及乳腺癌筛查研究者使用
乳腺摄影是乳腺癌筛查中最常用的影像手段，推动了深度学习技术在大规模分析中的应用。然而，准确且鲁棒的方法常受限于数据不足以及病灶特征多样性缺乏。生成模型虽可解决数据合成问题，但现有方法往往未能充分强调病灶特异性特征及其与周围组织的关系。本文提出门控条件扩散模型（GCDM），一种联合生成完整乳腺影像与局部病灶的新框架。GCDM基于潜空间去噪扩散架构，将加噪潜变量与表示乳腺、病灶及其过渡区域的软掩码嵌入拼接，确保去噪过程中解剖一致性。为强化病灶特异性特征，GCDM引入门控条件分支，动态选择并融合病灶的放射组学与几何属性，有效捕捉其相互作用。实验表明，GCDM能精确控制小病灶区域，同时提升合成影像的真实感与多样性。该成果为临床乳腺影像合成提供了有力工具。代码已开源：https://github.com/lixinHUST/Gated-Conditional-Diffusion-Model/
原文摘要 · Abstract (English)
Mammography is the most commonly used imaging modality for breast cancer screening, driving an increasing demand for deep-learning techniques to support large-scale analysis. However, the development of accurate and robust methods is often limited by insufficient data availability and a lack of diversity in lesion characteristics. While generative models offer a promising solution for data synthesis, current approaches often fail to adequately emphasize lesion-specific features and their relationships with surrounding tissues. In this paper, we propose Gated Conditional Diffusion Model (GCDM), a novel framework designed to jointly synthesize holistic mammogram images and localized lesions. GCDM is built upon a latent denoising diffusion framework, where the noised latent image is concatenated with a soft mask embedding that represents breast, lesion, and their transitional regions, ensuring anatomical coherence between them during the denoising process. To further emphasize lesion-specific features, GCDM incorporates a gated conditioning branch that guides the denoising process by dynamically selecting and fusing the most relevant radiomic and geometric properties of lesions, effectively capturing their interplay. Experimental results demonstrate that GCDM achieves precise control over small lesion areas while enhancing the realism and diversity of synthesized mammograms. These advancements position GCDM as a promising tool for clinical applications in mammogram synthesis. Our code is available at https://github.com/lixinHUST/Gated-Conditional-Diffusion-Model/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。