A Self-Refining Framework for Intracranial Primary Tumors Diagnosis

Accurate preoperative MRI diagnosis of intracranial primary tumors is critical for surgical planning and therapeutic decision-making. This study addresses two fundamental limitations of MRI-based diagnosis: the inherent class imbalance and resolution variations. To address these issues, we propose a novel self-refining framework that integrates three key innovations: (1) panoptic segmentation for unified representation of tumors and brain anatomy, (2) patch-wise cross-modality attention enabling adaptive feature fusion from multi-modal MRI, and (3) a dynamic loss function that automatically rebalances learning to prioritize rare tumor subtypes. Our method achieves state-of-the-art performance across multiple evaluation metrics, demonstrating consistent superiority over existing architectures while maintaining robustness to varying image resolutions. These technical advances can translate to clinical benefits, including improved detection of diagnostically challenging cases, anatomically plausible tumor segmentation for surgical planning, and reduced dependence on uniformly high-resolution scans, making the framework particularly valuable for real-world clinical deployment.