Published Paper Details:

Recent advances in deep learning have revolutionized medical imaging, particularly in the domain of tumor segmentation. This study introduces an innovative framework that integrates high-resolution imaging data with detailed clinical notes using state-of-the-art deep learning models and large language models (LLMs). In our approach, convolutional neural networks (CNNs) are employed to analyze imaging modalities, while transformer-based LLMs process unstructured clinical narratives to extract valuable contextual information. By merging these complementary data sources, the model achieves enhanced delineation of tumor boundaries and improved segmentation accuracy. Extensive experiments on diverse datasets reveal that the combined analysis mitigates common challenges such as imaging noise, variable tumor morphology, and limited contrast in tumor regions. The integration of clinical notes enriches the imaging analysis by providing patient history, biomarker information, and treatment context, thereby enabling more personalized segmentation outcomes. Results indicate a significant reduction in segmentation errors and a notable increase in model robustness, highlighting the potential of multi-modal fusion in clinical applications. This research underscores the critical role of combining imaging data with textual clinical information to overcome the limitations of single-modality approaches. Future directions include refining the fusion algorithms, expanding the framework to other cancer types, and real-time implementation in clinical settings to support diagnostic decision-making and personalized treatment planning. This innovative methodology adapts robustly across varied clinical scenarios, underscoring its promise for integration in routine oncological diagnostics.