Prompt-Driven Multimodal Segmentation with Dynamic Fusion for Adaptive and Robust Medical Imaging with Applications to Cancer Diagnosis.

BACKGROUND/

objectivesMedical image segmentation is a crucial task for diagnosis, treatment planning, and monitoring of cancer; however, it remains one of the toughest nuts to crack for Artificial Intelligence (AI)-based clinical applications. Deep-learning models have shown near-perfect results for narrow tasks such as single-organ Computed Tomography (CT) segmentation. Still, they fail to deliver under practicality, in which cross-modality robustness and multi-organ delineation are essential (e.g., liver Dice dropping to 0.88 ± 0.15 in combined CT-MR scenarios). That fragility exposes two structural gaps: (i) rigid task-specific architectures, which are not flexible enough to adapt to various clinical instructions, and (ii) the assumption that a universal loss function is best in all cancer imaging applications.

methodsA novel multimodal segmentation framework is proposed that combines natural language prompts and high-fidelity imaging features through Feature-wise Linear Modulation (FiLM) and Conditional Batch Normalization, enabling a single model to adapt dynamically across modalities, organs, and pathologies. Unlike preceding systems, the proposed approach is prompt-driven, context-aware, and end-to-end trainable to ensure alignment between computational adaptability and clinical decision-making.

resultsExtensive evaluation on the Brain Tumor Dataset (cancer-relevant neuroimaging) and the CHAOS multi-organ challenge demonstrates two key insights: (1) while Dice loss remains optimal for single-organ tasks, (2) Jaccard (IoU) loss outperforms when multi-organ, cross-modality divides cancer segmentation boundaries. Empirical evidence has thus been offered that optimality of a loss function is task- and context-dependent and not universal.

conclusionsThe design framework's principles directly address what is documented in workflow requirements and display capabilities that may connect algorithmic innovation with clinical utility once validated through prospective clinical trials.