图谱已内蕴其中：从预训练扩散模型中恢复人群模板
Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models
- KAUST（阿卜杜拉国王科技大学）
- Miami University（迈阿密大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种无需重训练的扩散模型推理时采样器，可从任意随机种子收敛至人群中心解剖结构（内在图谱），适用于多领域及亚人群，且作为配准目标性能最优或次优。
AI中文摘要：
我们提出了一种新的扩散模型推理时采样器，赋予预训练模型一项其从未被训练过的能力：构建其所合成人群的图谱。该采样器从每个随机种子收敛到人群的中心解剖结构，我们称之为“内在图谱”。其优势有三重：（1）无需重新训练。一个已经学习到连贯人群的扩散模型（包括已发布的模型）即可在单次推理过程中得到其图谱，无需涉及可变形配准。（2）适用于多个领域，如脑部MRI、胸部X光片、人脸和3D形状。（3）可扩展到亚人群。一个年龄条件模型可在其训练范围内的任意年龄生成图谱，且所得图谱族再现了健康衰老中脑脊液的扩张。作为配准目标进行评估时，内在图谱在每一个数据集上相对于经典和学习型模板均达到最佳或次佳，并且在留出的脑部MRI队列中是最中心的模板。图谱构建可以重新定义为生成建模的副产品：扩散模型是人群结构的习得表示，而图谱正是其已经包含的内容。
英文摘要：
We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas of the population it synthesizes. The sampler converges from every random seed to the population's central anatomy, which we call the \emph{intrinsic atlas}. The advantage is threefold. (1) It requires no retraining. A diffusion model that has already learned a coherent population, including the released ones, yields its atlas in a single inference pass without involving deformable registration. (2) It applies to multiple domains, such as brain MRI, chest X-ray, faces, and 3D shapes. (3) It extends to subpopulations. One age-conditioned model gives an atlas at any age in its training range, and the resulting family reproduces the CSF expansion of healthy aging. Evaluated as a registration target, the intrinsic atlas is best or second-best on every dataset against classical and learned templates, and the most central template on held-out brain MRI cohorts. Atlas construction can be reframed as a byproduct of generative modeling: a diffusion model is a learned representation of population structure, and the atlas is what it already contains.
