A multimodal transformer architecture designed for medical visual question answering, achieving state-of-the-art performance through advanced cross-modal attention mechanisms.
- Integration of transformer-based language models with MaxVit vision architecture
- Masked Language Modeling with image features as pretext task
- Optimized attention mechanisms for precise medical image region analysis
- 83.1% modality accuracy on VQA-Med dataset
- Enhanced semantic representations for medical image-text understanding
Comprehensive technical details and architecture diagrams coming soon.
Medical VQA represents a critical challenge in AI-assisted healthcare, requiring models to understand both complex medical imagery and natural language questions about diagnoses, treatments, and anatomical structures.
Our approach combines the latest advances in vision transformers with specialized medical domain knowledge, enabling precise analysis of specific image regions relevant to clinical questions. The 83.1% accuracy demonstrates significant progress toward practical medical AI assistants.
Detailed implementation insights and experimental results will be shared soon.