Neural Networks and NLP for Medical Imaging Report Generation: A Synergistic Approach

This paper addresses the challenge of automatic medical imaging report generation and proposes an advanced technical solution leveraging deep neural networks and natural language processing (NLP). The study designs a multi-stream convolutional neural network (CNN) to extract imaging features and constructs an improved Transformer model for sequence generation. An innovative integration of a medical knowledge graph within the NLP framework effectively standardizes medical terminology and models long-range semantic dependencies. The proposed approach employs hierarchical attention mechanisms and knowledge-constrained optimization to significantly enhance the professionalism and accuracy of generated reports. This method provides a novel technical perspective for intelligent medical diagnosis, contributing to improved efficiency and consistency in clinical decision-making.