In recent years, self-supervised learning has demonstrated state-of-theart performance in domains such as computer vision and natural language processing. However, fine-tuning these models for specific classification tasks, particularly with labeled data, remains challenging. This thesis introduces a novel approach to fine-tuning self-supervised models using Siamese Neural Networks, specifically leveraging a semi-hard triplet loss function. Our method aims to refine the latent space representations of self-supervised models to improve their performance on downstream classification tasks. The proposed framework employs Masked Autoencoders for pre-training on a comprehensive radiograph dataset, followed by fine-tuning with Siamese networks for effective feature separation and improved classification. The approach is evaluated on the COVIDx dataset for COVID-19 detection from frontal chest radiographs, achieving a new record accuracy of 98.5 percent, surpassing traditional fine-tuning techniques and COVID-Net CRX 3. The results demonstrate the effectiveness of our method in enhancing the utility of self-supervised models for complex medical imaging tasks. Future work will explore the scalability of this approach to other domains and the integration of more sophisticated embedding-space loss functions.