分离解剖结构与疾病进展，可控生成溃疡性结肠炎不同阶段内镜图像。
Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
- 用双嵌入控制解剖与疾病，通过注意力机制净化解剖特征。
- 在LIMUC数据集上生成高质量图像，改善分类任务性能。
- 无需额外推理，单次前向即可精确控制疾病进展方向。
在保持患者特异性解剖结构的同时，可控生成溃疡性结肠炎（UC）不同严重程度的纵向内镜图像，受限于病理纹理与结构特征的纠缠。针对按梅奥内镜评分（MES）连续有序进展的病症，我们提出解离式解剖-疾病扩散模型（DADD）。该框架基于两个互补嵌入：预训练图像编码器用于患者解剖，独立训练的序数嵌入器用于累积疾病严重程度。由于图像嵌入不可避免包含疾病信息，我们引入特征净化器（Feature Purifier），一种基于交叉注意力的擦除机制，识别并抑制与疾病相关的通道，获得纯净解剖表示。这些净化后的解剖标记与目标疾病标记通过分辨率依赖路由门的三路交叉注意力机制注入去噪网络，利用U-Net层级结构，深层编码全局结构，浅层捕捉细粒度病理纹理。此外，我们提出Delta Steering——一种从序数嵌入衍生的训练无关方向信号，实现推理时单次前向即可精确控制疾病过渡。在LIMUC数据集上验证，该方法在所有严重程度下生成高保真图像，有效缓解类别分布偏斜，提升下游分类性能。数据集见zenodo.org/records/5827695，代码见github.com/umutdundar99/progressive-stable-diffusion。
原文摘要 · Abstract (English)
Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pathological textures and structural features. We address this challenge for ulcerative colitis (UC) endoscopy, where severity follows a continuous ordinal progression along the Mayo Endoscopic Score (MES). Our framework, Disentangled Anatomy-Disease Diffusion (DADD), conditions a latent diffusion model on two complementary embeddings: a pretrained image encoder for patient anatomy and a separately trained ordinal embedder for cumulative disease severity. Since image embeddings inevitably capture disease information, we introduce a Feature Purifier, a cross-attention-based erasure mechanism that identifies and suppresses disease-correlated channels, yielding purified anatomical representations. These cleaned anatomy tokens and target disease tokens are injected into the denoising network via a Triple-Pathway Cross-Attention mechanism with resolution-dependent routing gates. This architecture leverages the U-Net hierarchy, in which different network depths encode global structure versus fine-grained pathological texture. Furthermore, we introduce Delta Steering, a training-free directional signal derived from the ordinal embeddings that enables explicit, single-pass control over disease transitions at inference without requiring additional forward passes. Validated on the LIMUC dataset, our approach produces high-fidelity images across all severity levels and effectively rebalances skewed class distributions, enhancing performance for downstream classification tasks. The dataset is available at zenodo.org/records/5827695 and the code base at github.com/umutdundar99/progressive-stable-diffusion
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