PathCo-LatticE: 一种受病理约束的专家 lattice 框架，用于全监督的少样本心脏 MRI 分割
PathCo-LatticE: Pathology-Constrained Lattice-Of Experts Framework for Fully-supervised Few-Shot Cardiac MRI Segmentation
AI总结：
PathCo-LatticE通过病理引导的合成监督和动态专家lattice框架，在少样本心脏MRI分割中实现优于现有方法的性能，并在无目标域微调的情况下实现接近全监督的准确率。
摘要：
少样本学习（FSL）缓解了心脏MRI分割中的数据稀缺问题，但通常依赖于对领域偏移和验证偏差敏感的半监督技术，限制了零样本泛化能力。我们提出了PathCo-LatticE，一种完全监督的FSL框架，通过病理引导的合成监督替代未标记数据。首先，我们的虚拟患者引擎从稀疏的临床锚点建模连续的潜在疾病轨迹，利用生成建模合成出生理上合理且完全标注的3D群体。其次，自我强化交错验证（SIV）提供了一种无泄漏的协议，通过逐步增加难度的合成样本在线评估模型，消除了对真实验证数据的需求。最后，动态的专家lattice（LoE）在病理意识的拓扑结构中组织专门的网络，并根据输入激活最相关的专家，使模型能够鲁棒地泛化到未见数据，而无需目标域微调。我们在严格的分布外（OOD）设置中评估了PathCo-LatticE，所有锚点和严重程度统计均从单一源域（ACDC）中推导，并在多中心、多供应商的M&Ms数据集上进行零样本测试。PathCo-LatticE在仅使用7个标注锚点的情况下，比四个最先进的FSL方法在Dice系数上高出4.2-11%，并且仅使用19个标注锚点即可达到全监督性能（误差在1%以内）。该方法在四个供应商之间表现出优越的协调性，并能泛化到未见的病理情况。[代码将公开发布]
英文摘要：
Few-shot learning (FSL) mitigates data scarcity in cardiac MRI segmentation but typically relies on semi-supervised techniques sensitive to domain shifts and validation bias, restricting zero-shot generalizability. We propose PathCo-LatticE, a fully supervised FSL framework that replaces unlabeled data with pathology-guided synthetic supervision. First, our Virtual Patient Engine models continuous latent disease trajectories from sparse clinical anchors, using generative modeling to synthesize physiologically plausible, fully labeled 3D cohorts. Second, Self-Reinforcing Interleaved Validation (SIV) provides a leakage-free protocol that evaluates models online with progressively challenging synthetic samples, eliminating the need for real validation data. Finally, a dynamic Lattice-of-Experts (LoE) organizes specialized networks within a pathology-aware topology and activates the most relevant experts per input, enabling robust zero-shot generalization to unseen data without target-domain fine-tuning. We evaluated PathCo-LatticE in a strict out-of-distribution (OOD) setting, deriving all anchors and severity statistics from a single-source domain (ACDC) and performing zero-shot testing on the multi-center, multi-vendor M&Ms dataset. PathCo-LatticE outperforms four state-of-the-art FSL methods by 4.2-11% Dice starting from only 7 labeled anchors, and approaches fully supervised performance (within 1% Dice) with only 19 labeled anchors. The method shows superior harmonization across four vendors and generalization to unseen pathologies. [Code will be made publicly available].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
