Abstract
Mammogram image analysis has benefited from advancements in artificial intelligence (AI), particularly through the use of Siamese networks, which, similar to radiologists, compare current and prior mammogram images to enhance diagnostic accuracy. One of the main challenges in employing Siamese networks for this purpose is selecting an effective distance function. Given the complexity of mammogram images and the high correlation between current and prior images, traditional distance functions in Siamese networks often fall short in capturing the subtle, non-linear differences between these correlated features. This study explores the impact of incorporating non-linear and correlation-sensitive distance functions within a Siamese network framework for analyzing paired mammogram images. We benchmarked different distance functions, including Euclidean, Manhattan, Mahalanobis, Radial Basis Function (RBF), and cosine, and introduced a novel combination of RBF with Matern Covariance. Our evaluation revealed that the RBF with Matern Covariance consistently outperformed other functions, emphasizing the importance of addressing non-linearity and correlation in this context. For instance, the ResNet50 model, when paired with this distance function, achieved an accuracy of 0.938, sensitivity of 0.921, precision of 0.955, specificity of 0.958, F1 score of 0.930, and AUC of 0.940. We observed similarly strong performance across other models as well. Furthermore, the robustness of our approach was confirmed through evaluation on a dataset of 30 cross-validation samples, demonstrating its generalizability. These findings underscore the effectiveness of non-linear and correlation-based distance functions in Siamese networks for improving the performance and generalization of mammogram image analysis. All codes used in this paper are available at https://github.com/NabaviLab/Benchmarking_Distance_Functions_in_Siamese_Networks.