The CA-TriNet model introduces a specialized architecture combining transformers with Multi-LSTM networks to overcome limitations of general large language models in medical image interpretation.

• Employs innovative Co-Attention module for synergistic feature extraction
• Utilizes Triple-LSTM network to address data repetition and similarity challenges
• Designed specifically to capture medical domain expertise that general models miss
• Demonstrates significant performance improvements on medical imaging datasets

This research addresses a critical healthcare need for accurate, automated medical report generation, potentially reducing radiologist workload while maintaining diagnostic accuracy.

Original Paper: Image-to-Text for Medical Reports Using Adaptive Co-Attention and Triple-LSTM Module