A vision attention driven Language framework for medical report generation

Area of Science:

• Artificial Intelligence
• Medical Imaging Analysis
• Natural Language Processing

Background:

• Automated medical report generation is crucial for efficient clinical workflows.
• Existing methods often struggle with semantic coherence and diagnostic accuracy in report generation.

Purpose of the Study:

• To introduce the Medical Vision Attention Generation (MedVAG) model for automated medical report generation.
• To enhance semantic coherence and diagnostic accuracy in AI-generated medical reports.

Main Methods:

• The MedVAG model integrates Vision Transformer (ViT) for visual feature extraction and GPT-2 for language modeling.
• Graph-based feature fusion and multiple attention mechanisms (co-attention, cross-attention, memory-guided attention) are employed.
• The model was evaluated on the IU X-Ray and COV-CTR datasets.

Main Results:

• MedVAG achieved state-of-the-art performance on natural language generation metrics (BLEU, METEOR, ROUGE, CIDEr).
• Clinical effectiveness measures demonstrated the model's diagnostic accuracy.
• Ablation studies confirmed the importance of attention mechanisms and feature fusion.

Conclusions:

• MedVAG shows significant potential as an assistive technology for radiologists.
• The model can help reduce radiologist workload and improve diagnostic accuracy.
• Advanced attention and fusion techniques are key to aligning visual and textual information for medical report generation.