用少量数据生成高质量脑瘤MRI，提升低资源医院诊断能力
Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis
- 先学健康脑结构，再用肿瘤图约束生成病灶，实现少样本3D MRI合成
- 仅用16张目标数据就达到FID 85.40、分类AUC 0.987，优于传统方法
- 适合医疗数据稀缺场景，尤其对脑瘤影像生成与增强研究者有用
弥漫性胶质瘤的准确分类常因中心间数据分布差异及缺乏大规模标注数据集而受阻。本文提出解剖结构引导的潜在扩散模型（ALDM），一种高效、少样本的3D体积MRI生成框架。ALDM采用两阶段设计：首先通过3D变分自编码器从数据丰富的源域学习解剖先验；随后，基于控制网络（ControlNet）以肿瘤掩码为条件的潜在扩散模型，在数据稀疏的目标域生成结构一致的3D图像。在极端少样本设置下（仅16张目标图像），ALDM超越了GAN和混合基线，取得85.40的弗雷切特起始距离（FID）和0.987的下游分类AUC。定性结果显示，模型保持了清晰的病理边界和跨模态一致性，且生成质量随训练逐步提升。通过捕捉关键诊断特征，ALDM为低资源临床环境下的数据增强提供了可靠工具。代码已开源：https://github.com/Analytics-Everywhere-Lab/anatomically-conditioned-LDM。
原文摘要 · Abstract (English)
Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two-stage approach: a 3D variational autoencoder learns anatomical priors from a data-rich source domain, while a conditional latent diffusion model, guided by tumor masks via a ControlNet, generates structurally coherent volumes for a data-scarce target domain. Evaluated in an extreme few-shot setting with only 16 target images, ALDM outperformed GAN and hybrid baselines, achieving a superior Frechet Inception Distance (FID) of 85.40 and a downstream classification AUC of 0.987. Qualitative results confirm that the model preserves sharp pathology boundaries and cross-modal consistency, with visual fidelity improving progressively during training. By capturing essential diagnostic features, ALDM provides a robust tool for clinical data augmentation in low-resource settings. Our implementation is available at https://github.com/Analytics-Everywhere-Lab/anatomically-conditioned-LDM.
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