Abstract
Conventional single-dataset training often fails with new data distributions, especially in ultrasound (US) image analysis due to limited data, acoustic shadows, and speckle noise. Therefore, constructing a universal framework for multi-heterogeneous US datasets is imperative. However, a key challenge arises: how to effectively mitigate inter-dataset interference while preserving dataset-specific discriminative features for robust downstream task? Previous approaches utilize either a single source-specific decoder or a domain adaptation strategy, but these methods experienced a decline in performance when applied to other domains. Considering this, we propose a Universal Collaborative Mixture of Heterogeneous Source-Specific Experts (COME). Specifically, COME establishes dual structure-semantic shared experts that create a universal representation space and then collaborate with source-specific experts to extract discriminative features through providing complementary features. This design enables robust generalization by leveraging cross-datasets experience distributions and providing universal US priors for small-batch or unseen data scenarios. Extensive experiments under three evaluation modes (single-dataset, intra-organ, and inter-organ integration datasets) demonstrate COME's superiority, achieving significant mean AP improvements over state-of-the-art methods. Our project is available at: this https URL.
Abstract (translated)
传统的单一数据集训练在面对新的数据分布时往往效果不佳，特别是在超声（US）图像分析中，由于数据量有限、声影和斑点噪声的影响更为显著。因此，构建一个多异构US数据集的通用框架至关重要。然而，一个关键挑战是如何有效地减少跨数据集干扰的同时保留特定数据集特有的判别性特征，以增强下游任务的鲁棒性。先前的方法要么使用单一的数据源特异性解码器，要么采用领域适应策略，但这些方法在应用于其他领域时性能有所下降。 鉴于此，我们提出了一种通用协作异构来源特异性专家混合模型（COME）。具体而言，COME建立了结构-语义共享的双专家体系，创建了一个通用表示空间，并且与特定数据源的专家进行合作，通过提供互补特征来提取判别性特征。这种设计通过利用跨数据集的经验分布并为小批量或未见过的数据场景提供通用的US先验知识，实现了稳健泛化。 在三种评估模式（单一数据集、同一器官内的整合数据集和不同器官间的整合数据集）下进行的广泛实验表明，COME具有优越性，并且在最先进的方法中取得了显著的平均AP改进。我们的项目可在此链接访问：this https URL。
URL
https://arxiv.org/abs/2508.09886
