Leveraging Advanced Vision-Language Model in Medical Visual Question Answering

Visual Question Answering (VQA) in healthcare represents a groundbreaking method that utilizes sophisticated computational tools to interpret intricate details within medical images. These images often involve intricate question variations and high degrees of abstraction, posing significant challenges for traditional analysis methods. This study examines the BLIP model, which integrates Vision Transformers (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V$</tex>iT) for robust image encoding with transformers for nuanced text encoding, aiming to address these challenges effectively. Our comprehensive evaluation on the Path Vqa dataset shows the model's accuracy at 83.91%, including 97.43% for close-ended questions and 66.15% for open-ended questions. Similarly, on the VQA-RAD dataset, the model achieves an overall accuracy of 82.86%, with 87.85% for close-ended questions and 76.82% for open-ended questions. These results demonstrate the BLIP model's adeptness at managing different types of questions, highlighting its potential to significantly enhance diagnostic processes and decision-making in medical VQA applications, this renders it an essential resource in clinical environments and medical investigations.