发表机构
University of Florence; University of Trento（佛罗伦萨大学; 特伦托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出WASP框架，利用临床元数据驱动的熵最优传输构建弱对齐时空对，将MRI表示映射到超声潜在空间，无需配对数据即可提升仅超声模型的胎儿MRI理解能力，显著降低GA估计误差并提高平面分类准确率。
AI 中文摘要
磁共振成像（MRI）因其优越的软组织对比度和解剖细节，被广泛认为是胎儿脑部分析的最佳传感器。然而，其高昂的成本和操作负担使其具有侵入性且难以大规模获取。相比之下，超声（US）便宜、安全且常规采集，因此产生了大量数据集和不断增长的预训练模型生态系统。这种不对称性引发了一个自然问题：我们能否仅通过有限的示例教会一个仅使用超声的模型理解胎儿MRI？标准的做法是在受试者配对的MRI-US扫描上训练基础模型，但由于没有公开可用的配对胎儿数据集，这种方法不可行。在本文中，我们通过弱对齐时空对（WASP）解决了这一差距，该框架将跨模态对应关系表述为受临床元数据（特别是胎龄（GA）和诊断平面）驱动的熵最优传输问题，从而能够拟合一个轻量级对齐模块，将MRI表示提升到US潜在空间，而无需微调主干网络。实验上，当MRI在预训练期间未被模型见过时（在USFM上，GA估计误差从21.9天降至17.4天，标准平面分类准确率从61.9%提升至69.0%），WASP取得了最大收益，同时为在两种模态上预训练的主干网络提供了较小的、依赖于主干的改进（例如，BioMedParse的GA估计误差从6.5天降至6.0天，SAM-Med2d的平面准确率从83.3%提升至88.1%）。代码可在该https URL获取。
英文摘要
Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomical detail. However, its high cost and operational burden make it invasive and difficult to obtain at scale. Ultrasound (US), in contrast, is cheap, safe, and routinely acquired, and as a result it has produced substantially larger datasets and a growing ecosystem of pretrained models. This asymmetry raises a natural question: Can we teach a US-only model to understand fetal MRI from only a limited set of examples? The standard recipe, training a foundation model on subject-to-subject paired MRI-US scans, is not viable since no such paired fetal dataset is publicly available. In this paper we address this gap with Weakly Aligned Spatiotemporal Pairs (WASP), a framework that formulates cross-modal correspondence as an entropic Optimal Transport problem driven by clinical metadata, in particular Gestational Age (GA) and diagnostic planes, enabling the fitting of a lightweight alignment module that lifts MRI representations into the US latent space, without fine-tuning the backbone. Empirically, WASP yields its largest gains when MRI is unseen by the model during pretraining (on USFM, GA estimation error drops from 21.9 to 17.4 days and standard plane classification accuracy climbs from 61.9% to 69.0%), while providing smaller, backbone-dependent refinements for backbones pretrained on both modalities (e.g., BioMedParse GA estimation error from 6.5 to 6.0 days and SAM-Med2d plane accuracy from 83.3% to 88.1%). Code is available at https://github.com/miccunifi/WASP.
CommentsAccepted at NeurIPS 2026. 21 pages, 3 figures, 11 tables. Code: https://github.com/miccunifi/WASP
