Bridging the Gap: Leveraging Textual and Visual Contexts for PreciseMedical Visual Question Answering

The advent of Visual Question Answering (VQA) technology has brought significant advancements in the medical field, offering transformative potential in clinical diagnostics and patient care. This research explores the application of VQA within the medical domain, highlighting its critical role in interpreting complex visual data, such as radiological images, pathology slides, and other diagnostic visuals. Traditional diagnostic processes often rely heavily on human expertise, which can be time-consuming and prone to variability. VQA systems, powered by sophisticated machine learning models, provide consistent and accurate interpretations, thus enhancing diagnostic accuracy and efficiency. Visual Question Answering (VQA) in the medical field necessitates extracting information from both textual and visual inputs to provide accurate answers, a critical requirement for supporting medical decision-making. This research introduces a novel approach to address VQA challenges in the medical domain using Bi-Directional Layout with Positional Encoding (BLIP) models. Our methodology seamlessly integrates text and image processing within a unified framework, enabling precise interactions between textual queries and medical imaging data. We commence with textual inputs, encoded by BLIP processors, and medical images, encoded by BLIP image processors. A custom VQA dataset, specifically designed for the medical field, includes textual questions and their corresponding medical image features. We employ a BLIP-based Question Answering architecture, fine-tuned on our medical VQA dataset, and optimized using the AdamW optimizer with a learning rate of 0.00005, ensuring efficient convergence. Additionally, we introduce attention mechanisms using Coarse and Fine Attention blocks for enhanced feature fusion and accurate answer prediction. Our results are highly encouraging, demonstrating competitive metrics in extensive VQA task experiments on both training and validation datasets. Qualitative analysis of sample predictions indicates the model’s capability to provide accurate answers for diverse visual and textual medical inputs. This work holds significant promise for improving automated medical image analysis and supporting clinical decision-making.