COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets

TL;DR

This paper introduces COME, a dual structure-semantic learning framework with collaborative experts, to improve universal lesion detection across diverse ultrasound datasets by mitigating inter-dataset interference and preserving dataset-specific features.

Contribution

COME is the first to integrate dual shared and source-specific experts for robust multi-heterogeneous ultrasound dataset learning, enhancing generalization and detection accuracy.

Findings

Achieves significant AP improvements over state-of-the-art methods.

Demonstrates robustness across single, intra-organ, and inter-organ datasets.

Provides universal ultrasound priors for small-batch and unseen data scenarios.

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: https://universalcome.github.io/UniversalCOME/.