# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 2 Related Work  
Deep learning has increasingly become pivotal in medical image analysis, with particular success in carotid plaque classification within single-center studies [8; 10; 11; 12]. Convolutional Neural Networks (CNNs) are highly proficient at extracting complex features from ultrasound images, significantly boosting the classification accuracy [13]. For example, [PERSON] et al. [14] pioneered NDDR-LCS, a multitask learning approach that leverages CNNs and data from ultrasound reports to enhance the precision of plaque classification. This  
Figure 1: Carotid ultrasound in different views: (**a**) is a longitudinal section, while (**b**) is a transverse section. approach has led to substantial advancements in single-center plaque classification, offering essential diagnostic tools. However, the models adapted to single-center data often cannot be applied to other datasets without experiencing a decline in performance, thus necessitating robust multicenter carotid plaque classification models [15]. Despite the limited number of studies specifically targeting multicenter plaque classification, significant progress has been observed in the multicenter studies within the various domains [16]. To address these challenges, Domain Adaptation [17] has become critical, such as Domain-Adversarial Neural Networks (DANNs) [18] and Maximum Classifier Discrepancy (MCD) [19]. A DANN employs adversarial training to minimize the domain discrepancies by forcing the model to learn features that are invariant across different domains, thereby enhancing the generalization. On the other hand, MCD utilizes a pair of classifiers in an adversarial setting to align the domain features effectively, focusing on maximizing the discrepancy between the classifier outputs to better adapt to new domains. These techniques extend the applicability of the machine learning models to multicenter data, ensuring that the advancements in single-center studies translate effectively into more complex multicenter environments. These advancements in single-source domain adaptation pave the way for exploring more complex scenarios involving multiple source domains. As the medical datasets often come from various centers, each with distinct data characteristics, Multi-Source Domain Adaptation (MSDA) becomes essential. Multi-Source Domain Adaptation (MSDA) represents a significant branch of machine learning that focuses on transferring knowledge from various source domains to a single target domain [20]. The necessity for MSDA arises due to the varying and complex data distributions across domains, which the single-source domain transfer learning models cannot sufficiently address. The approach involves integrating the data from multiple sources, often utilizing strategies that adjust for the differences in both the marginal and conditional probabilities relative to the target domain. This method ensures that the knowledge from diverse domains is effectively utilized to improve the predictive accuracy and model robustness. The significant contributions to this field include a two-stage weighting framework that strategically combines multi-source data [21]. However, this framework may struggle with the scalability and complexity of high-dimensional data, potentially limiting its effectiveness in more intricate scenarios. Moment Matching for Multi-Source Domain Adaptation (M3 SDA) [22] is a technique that dynamically aligns feature distributions to bridge domain gaps effectively. Additionally, [PERSON] et al. [23] explored distance-based measures to assess the domain dissimilarities, particularly in natural language processing tasks, enhancing the text classification under domain variation. Further innovations include the Multi-source Sentiment Generative Adversarial Network (MSGAN) [24], which addresses the visual sentiment classification challenges under multi-source scenarios. This method simply averages the prediction results from several classifiers without specifically adjusting the contribution of each classifier.