# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 4 Results
### Experimental Results  
#### 4.2.1 Model Comparison  
This section compares the WUCF model with other methods, categorized into three types:  
1. Source-only methods: the source domain data are used for fully supervised training, with direct application to the target center data without any domain adaptation, using Resnet-50 as the base model. 2. Source-combine methods: this involves combining source centers into a single source for single-source domain adaptation training--a DANN [18], which uses adversarial training to help models generalize across different domains by learning domain-invariant features and MCD [19], which leverages adversarial techniques between multiple classifiers to align domain features, thereby enhancing the generalization across different domains. 3. Multi-source methods: MFSAN [27] aligns specific domain distributions and classifiers within a multi-source domain environment and averages the final classifier outputs to produce the ultimate prediction; MDAN [27] extends the adversarial learning strategies from addressing single-source center issues (DANN) to multi-source center domain adaptation problems; M3 SDA [22] is a domain adaptation method that reduces the distribution discrepancies by matching the statistical moments in the feature spaces between multiple source domains and the target domain, with a particular emphasis on matching higher-order statistical properties to achieve comprehensive domain adaptation effects; AMTLDC [30] proposes a CNN-based feature extraction network, followed by the construction of classifiers and domain discriminators. From Table 2, it is clear that various multi-source, multicenter domain adaptation methods outperform the unadapted baseline ResNet-50 model, demonstrating the necessity of employing multi-source domain adaptations for the issue of multicenter carotid artery plaque ultrasound classification. Additionally, the results from the multi-source methods are superior to those from the source-combine methods, indicating that utilizing the information from multiple source centers enhances the model generalization and classification accuracy more effectively than relying on a single source center's data. Compared with DANNs [31] and MCD [19], two single-source domain adaptation methods, the WUCF also showed improved performance. The major difference between the two types is that the data features are mapped to a common space in DANNs and MCD, whereas the WUCF performs targeted feature mapping, and the domain expert discriminator can more effectively and flexibly integrate this independent \"knowledge\". Furthermore, the WUCF outperformed the other comparative methods across three key performance metrics: accuracy, precision, and recall, confirming its significant advantages in the classification of multi-source center carotid ultrasound images. Analyzing the results of the other methods, MFSAN [29], despite considering the data differences between different source centers, merely averaged the final classifier results and failed to effectively integrate the heterogeneous knowledge from each source center. MDAN uses an adversarial learning strategy aimed at improving the generalizability by extracting more generalized feature representations, but its practical performance might be limited due to the design constraints of the adversarial model and the specific characteristics of the target domain data. Additionally, although M3 SDA assumes that all the source domains contribute similarly to the target domain, the actual contributions of different source domains to the target domain can vary significantly, and this oversimplified assumption might affect the method's adaptability and effectiveness. Lastly, the CNN-based feature extraction model proposed by AMTLDC may be overly complex for handling small medical datasets, resulting in performance that does not meet expectations. Therefore, the WUCF is