Beyond Static Knowledge: Dynamic Context-Aware Cross-Modal Contrastive Learning for Medical Visual Question...

Area of Science:

• Artificial Intelligence
• Medical Imaging Analysis
• Natural Language Processing

Background:

• Medical Visual Question Answering (Med-VQA) systems analyze medical images and answer questions to improve clinical workflows.
• Existing Med-VQA models face challenges due to information imbalance between images and queries, leading to semantic bias.
• Current methods often use static external knowledge, which can be redundant and introduce noise, hindering performance.

Purpose of the Study:

• To propose a novel Contextual Knowledge-Aware Dynamic Perception for Cross-Modal Reasoning and Alignment (CKRA) model for Med-VQA.
• To address information imbalance and cross-modal semantic bias in Med-VQA.
• To improve the accuracy and efficiency of medical image analysis through advanced AI.

Main Methods:

• CKRA utilizes a dynamic perception mechanism to filter relevant medical knowledge based on query semantics, reducing redundancy.
• Knowledge-image contrastive learning bridges visual and linguistic features, optimizing knowledge representation and focusing on key image regions.
• A dual-stream guided attention network facilitates multi-dimensional cross-modal interaction and alignment.

Main Results:

• The CKRA model demonstrated superior performance compared to state-of-the-art methods on the SLAKE and VQA-RAD datasets.
• Ablation studies confirmed the effectiveness of individual modules within the CKRA architecture.
• Grad-CAM visualizations validated the model's ability to focus on relevant image regions for medical visual questioning.

Conclusions:

• The proposed CKRA model effectively addresses semantic bias and knowledge redundancy in Med-VQA.
• CKRA significantly improves the performance of medical visual question answering tasks.
• The model's dynamic and context-aware approach offers a promising direction for AI in medical diagnostics.