# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
###### Abstract  
The rupture of unstable plaques is a major cause of acute cardiovascular events. The early assessment of carotid plaques can significantly reduce the cardiovascular risks, so developing evaluation models suitable for data from different centers is of great clinical importance. This study leverages plaque datasets from multiple centers to develop a Weighted multi-source carotid artery plaque Unsupervised Classification Framework (WUCF). The multi-source domain adaptation module of the WUCF focuses on maintaining feature consistency between each independent source and target center, while also integrating a specialized domain discriminator expert. This ensures that the knowledge from each source center is effectively learned and combined for accurate predictions in the target domain. The experimental evaluation of WUCF, using datasets from three centers, has demonstrated the method's superiority and robustness.  
deep learning; plaque; multicenter; multisource; domain adaptation +
Footnote †: journal: Medical  
failing to effectively classify the data from multi-source centers. Secondly, the model faces the issue of inconsistent ultrasound image views, meaning that different source centers contain different views. For example, in the processing of carotid artery plaque images, some source centers might provide datasets that include both longitudinal and transverse images of the carotid artery, while others might offer images from only one angle, as shown in Figure 1. Although this diversity in multi-source center situations adds complexity to the classification task, it also provides a potential for model design--by integrating the \"knowledge\" of different imaging characteristics and views from different source centers, the model can offer a more enriched set of information sources for the target center.  
To address the aforementioned challenges, this paper introduces a novel framework called the Weighted Unsupervised Classification Framework for Multi-Source Center Ultrasound Plaque Images (WUCF), which not only acknowledges the heterogeneity of the data across different source centers but also strives to explore how to efficiently integrate these data to enhance and optimize the classification performance in the target domain. Specifically, a new Multi-domain Adaption module with a domain Discrimination Expert (MADE) is designed in the WUCF. Considering that each source center possesses its unique knowledge, the model trains an expert classifier for each source center to better distinguish the domain characteristics of each source. Managing the integration of knowledge from multiple expert classifiers is crucial, as simply merging all source domains and applying a single-source unsupervised domain adaptation method may not be accurate. The MADE introduces a domain discrimination expert that uses coefficients learned by the domain discriminator to blend the predictions of domain experts. The contributions of this work are summarized as follows:  
* The WUCF is proposed for the early detection of and risk reduction in acute cardiovascular events caused by unstable plaque ruptures. This model is designed to handle the diverse ultrasound data from various medical centers.
* A Multi-source domain Adaptation module with a domain Discrimination Expert (MADE) is designed to dynamically adjust the weights and evaluate the contributions of different source domain expert classifiers. This approach aims to refine the integration of the source domain features, reduce negative transfer, and enhance the generalization capabilities across diverse domains.
* The extensive experiments conducted on three centers datasets have demonstrated the efficacy of the proposed WUCF.