M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation

The scarcity of labeled data poses challenges in Automated retinal image medical description generation is both image classification and caption generation tasks in crucial for streamlining medical diagnosis and treatment medical image analysis. Researchers address this by employing planning. Existing challenges include the reliance on learned Transfer Learning, leveraging models pre-trained on retinal image representations, difficulties in handling multiple ImageNet for medical image tasks [7, 8]. Pre-training on imaging modalities, and the lack of clinical context natural images and fine-tuning on medical datasets enhances in visual representations. Addressing these issues, we propose feature learning, especially in medical image classification the Multi-Modal Medical Transformer (M3T), a novel [9]. Semi-supervised and self-supervised learning in medical deep learning architecture that integrates visual representations representation explores unlabeled data, benefiting subsequent with diagnostic keywords.