SSCMIRG: Self-Supervised Contrastive Learning for Medical Images with Report Generation

Machine learning has shown promising applications in interpreting medical images, offering accurate results in areas lacking radiologists. While not replacing radiologists, image captioning assists in diagnosis and therapy. Self-supervised contrastive learning is vital for medical image analysis, inspired by human categorization learning. This technique classifies unlabeled data to generate reports, aiding doctors in making better diagnoses and treatment plans. By predicting and grouping image components, medical report formation ensures accurate and consistent record-keeping. Contrastive learning enables models to learn from unlabeled data by identifying similarities and differences. These advancements have become crucial in machine learning, enhancing medical imaging and opening doors for further improvements and research.