Аңдатпа
Anterior cruciate ligament (ACL) injuries are among the most common and clinically significant knee disorders, and accurate detection from MRI remains essential for timely intervention. Recent advances in deep learning have shown promising results in analyzing MRI scans for ACL diagnosis. At the same time, self-supervised learning (SSL) has emerged as a powerful strategy to learn robust feature representations from unlabeled data. In this work, we evaluate the use of the Bootstrap Your Own Latent (BYOL) method for pretraining a ResNet-18 encoder, which is subsequently employed for ACL tear detection. Specifically, the encoder is first pretrained on unlabeled MRI scans to generate feature embeddings. These embeddings are then transferred to a downstream classifier to assess their effectiveness in improving classification accuracy. • Leveraging self-supervised learning to extract informative features from unlabeled knee MRI data using the BYOL framework. • Employing a pretrained ResNet-18 encoder to enhance feature representation for anterior cruciate ligament tear detection.