# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 5 Conclusions  
To address the significant challenges in the early detection and risk reduction of acute cardiovascular events caused by unstable plaque ruptures, a multi-source, multicenter model for carotid plaque risk classification has been developed. This model is crucial given that carotid plaque ultrasound datasets often come from diverse medical centers, facing substantial challenges. The variations in medical practices and equipment across different centers lead to the discrepancies in data, while the inconsistencies in ultrasound views, such as longitudinal versus transverse scans, introduce potential negative transfer. These issues undermine the model's generalizability and can significantly diminish the accuracy when applying a model developed for a single center to multicenter data. This paper presents the innovative Weighted Unsupervised Classification Framework for multi-source center ultrasound plaque images (WUCF). Addressing the inherent challenges in multi-source ultrasound data, the MADE leverages the domain-specific fully connected layers, tailored for each source center, to create a phase of domain feature adaptation that efficiently aggregates the feature knowledge from various centers. Diverging from traditional methods, the MADE integrates a domain discrimination expert, enhancing the efficiency of the knowledge integration from multiple expert classifiers and boosting the overall classification performance. The extensive experiments across datasets from three centers, compared against source-only, source-combine, and multi-source methods, demonstrated the WUCF's superior performance. Furthermore, the tests contrasting longitudinal and transverse view differences validated the WUCF's robust classification capabilities amidst the view discrepancies. These results not only confirm the proposed framework's superiority in managing multi-source data but also offer new perspectives and methodologies for the future research in the complex data environments. The potential applications of the WUCF are extensive. It can ensure consistent data analysis across different research sites in multicenter clinical studies, enhancing the reliability of the study results. In telemedicine, the WUCF improves the reliability of remote diagnoses by accommodating the varied imaging protocols from different locations. Additionally, it assists clinicians in accurately identifying and classifying carotid plaques, streamlining the diagnostic workflows, and contributing to personalized treatment plans by providing accurate classifications. Despite the promising results, several limitations need to be addressed. The model faces scalability issues as the number of centers increases, requiring more computational resources and posing challenges for its real-time implementation. Additionally, the quality of unlabeled data is crucial for the WUCF's performance; poor-quality data with noise or artifacts can significantly degrade the results. The model also has interpretability challenges, making it difficult to understand the decision-making process, which may hinder the clinical acceptance and trust. Furthermore, the WUCF requires re-training for domain adaptation when encountering the data from new centers, limiting its direct applicability to new datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda\) & **Acc** & **Pre** & **Rec** \\ \hline
0.01 & 72.62 & 69.17 & 60.18 \\
0.02 & 73.79 & 75.89 & 73.42 \\
0.05 & 74.02 & 75.98 & 76.98 \\
0.10 & 74.30 & 76.34 & 77.32 \\
0.20 & 75.33 & 78.76 & 78.88 \\
0.50 & 76.77 & 80.58 & 79.29 \\
1 & 75.30 & 79.56 & 79.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determination of loss function coefficient. Future research could extend the framework to include more diverse data sources and datasets to further test its adaptability and generalization capabilities.