  Purpose: Our study presents an enhanced approach to medical image caption
generation by integrating concept detection into attention mechanisms. Method:
This method utilizes sophisticated models to identify critical concepts within
medical images, which are then refined and incorporated into the caption
generation process. Results: Our concept detection task, which employed the
Swin-V2 model, achieved an F1 score of 0.58944 on the validation set and
0.61998 on the private test set, securing the third position. For the caption
prediction task, our BEiT+BioBart model, enhanced with concept integration and
post-processing techniques, attained a BERTScore of 0.60589 on the validation
set and 0.5794 on the private test set, placing ninth. Conclusion: These
results underscore the efficacy of concept-aware algorithms in generating
precise and contextually appropriate medical descriptions. The findings
demonstrate that our approach significantly improves the quality of medical
image captions, highlighting its potential to enhance medical image
interpretation and documentation, thereby contributing to improved healthcare
outcomes.
