This research introduces a framework that combines semantic segmentation with LLMs to understand both visual features and contextual relationships between objects in images.

• Overcomes traditional segmentation limitations by distinguishing semantically similar objects (e.g., doctor vs. nurse)
• Enhances understanding of complex contextual scenarios in images
• Enables more accurate interpretation of medical imaging by incorporating contextual knowledge
• Bridges the gap between pixel-level classification and higher-order semantic understanding

For medical applications, this technology promises improved diagnostic accuracy through better identification of anatomical structures in context, enhanced medical image analysis, and more reliable computer-aided diagnosis systems.

Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applications