A Technical Evaluation of MedSigLIP for Mammography Classification via Feature Extraction and Fine-Tuning

Early and accurate diagnosis of breast cancer is critical for reducing mortality and improving patient outcomes, as it enables timely intervention and more effective treatment planning. In this context, developing reliable and high-performance automated systems for mammography analysis is of great clinical and societal importance. In this study, the classification efficiency of MedSigLIP, one of the most recent technologies in the medical domain, was evaluated on mammography images. Within this scope, both its feature extraction capability and fine-tuning model training competence were assessed, and comprehensive experiments were conducted. For the feature extraction task, features were extracted using MedSigLIP and SigLIP2, the state-of-the-art vision encoder, and their classification performances were reported using seven different machine learning methods. In addition, for the fine-tuning model training task, MedSigLIP was compared with six different convolutional neural network architectures, and the results were reported. According to the obtained results, MedSigLIP achieved the best performance in both tasks, producing 73% F1 score in the MLO view and 83% F1 score in the CC view for feature extraction, and 76% F1 score in the MLO view and 89% F1 score in the CC view for the fine-tuning task.