A SELF-SUPERVISED LEARNING MODEL FOR THE CLASSIFICATION OF BRAIN TUMORS USING MEDICAL IMAGES: A REVIEW (Journal of Medical & Health Sciences Review, 2025)
Brain tumors are critical neurological disorders, and early detection is essential for effective treatment. Traditional diagnostic methods, which rely on manual interpretation of medical images, are time-consuming, error-prone, and dependent on clinician expertise. With advancements in artificial intelligence (AI) and deep learning (DL), there has been significant progress in automating the detection and classification of brain tumors from medical images. However, a significant challenge remains: the limited availability of large, annotated datasets. Annotated data is expensive, scarce, and often subject to privacy concerns, making it difficult to fully leverage deep learning techniques. To address this issue, self-supervised learning (SSL) has emerged as a promising solution. SSL enables deep learning models to generate supervisory signals from unlabeled data, significantly reducing the need for manual annotation. This is particularly beneficial in medical imaging, where acquiring labeled data can be costly and time-consuming. SSL methods, such as contrastive learning, rotation prediction, and jigsaw puzzles, allow models to learn meaningful feature representations from unlabeled data, which can then be fine-tuned for tasks like tumor classification. Techniques like contrastive learning (e.g., SimCLR, MoCo, and BYOL), generative models (e.g., autoencoders and GANs), and clustering-based approaches (e.g., DeepCluster and SwAV) have shown success in learning from unlabeled medical images. In addition, SSL facilitates the integration of multiple imaging modalities, such as MRI, CT, and PET scans. By combining these modalities, SSL models can leverage complementary information, leading to enhanced tumor classification accuracy and robustness. Federated learning (FL) combined with SSL allows for collaborative model training across multiple institutions without sharing sensitive patient data, thus ensuring privacy. Despite the significant advancements in SSL for brain tumor classification, challenges remain. These include the need for small labeled datasets for fine-tuning, domain shifts across imaging modalities, interpretability issues, and the computational complexity of training deep SSL models. In conclusion, SSL has the potential to revolutionize brain tumor classification by reducing the reliance on large annotated datasets. Continued research into SSL techniques can lead to more accurate and efficient diagnostic tools, improving patient outcomes through earlier and more precise tumor detection.