# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 4 Discussion
The reason is that among the site image features extracted from AE and site average pooling, there are still features that can highly reflect the difference between sites except the features with high phenotypic information correlation. The distribution of these features has nothing to do with phenotypic information correlation, but only increases with the increase of the total number of features. When a certain threshold is exceeded, the number of features in this part is enough to enhance the ability of site features to reflect site differences again, and ultimately improve the accuracy. The experimental results of hyperparameter optimization show that different parameters will affect the classification accuracy, and there is an optimal parameter to make the accuracy highest. However, it can be seen from the experimental results that the accuracy difference between different parameters is small, and the accuracy difference between whether MS-AINN is used or not is large. Even when AE reduction and feature selection are not used, MS-AINN still has performance improvement. Therefore, it can be concluded that the reason for the effectiveness of the proposed method lies in the use of MS-AINN, and the purpose of the method is not to find the optimal parameter set, but to achieve anti-interference classification by means of the method of multi-site confounding removal. In the interpretability analysis of the model visualization, the results of the visualization of important brain regions show that the representation learning module can actively identify the brain regions that have changes due to disease, and extract the difference features for classification. Moreover, the high-contribution brain areas after using MS-AINN were the same as before, indicating that although MS-AINN limited the feature extraction of the representation learning module to remove site confusions, it did not affect the model's recognition of important brain areas. At the same time, parts of the brain that were bluer became bluer and less important. MS-AINN also makes the representational learning module focus more on important brain regions that may have physiological differences, while ignoring non-important brain regions. After reducing the weight value of non-critical brain regions, the features extracted contain the difference information of normal patients in important brain regions, and reduce the interference of confounding factors in non-important brain regions, and finally improve the classification performance. The T-SNE visualization of the representation learning features shows that MS-AINN can make the features separated from each other gather together. The reason for the change in spatial distribution is the heterogeneity of the data of multiple sites. The feature extraction directly through the representation learning module will be affected by this, resulting in the separation of the feature vector domain between sites. In order to extract the feature vectors of the mixed influence of the sites, MS-AINN limited the feature extraction process of the representation learning module by means of adversarial training, so that the solution space of the model was concentrated in the region that did not reflect the differences of the sites, and finally obtained the feature vectors of the inter-site domain aggregation.