DeepSite-MRI: A Public Benchmark for Deep-Seated Intracranial Tumor Diagnosis

Deep-seated intracranial tumors (thalamus, basal ganglia, brainstem) are adjacent to critical functional areas, making direct surgical resection typically infeasible; clinicians rely on stereotactic biopsy to determine subtypes for treatment planning. However, existing brain tumor datasets focus on superficial tumors, and no public benchmark with biopsy-confirmed labels exists for deep-seated tumors. To bridge this gap, we present DeepSite-MRI, the first such benchmark, comprising 249 biopsy-confirmed cases across four subtypes (Glioma, PCNSL, Germinoma, Rare), together with a Two-Stage spatial Prior pipeline (TSP) that combines VLM-guided coarse localization with MLP-based refinement, reducing per-case annotation time from approximately 90 minutes to approximately 3 minutes (96% clinical acceptance rate); this paradigm is transferable to other data-scarce medical imaging domains. Meanwhile, we systematically evaluate 10 mainstream architectures spanning CNNs and Transformers to characterize the difficulty and research value of this benchmark. Results show that even well-established methods that perform strongly on large-scale brain tumor benchmarks underperform on DeepSite-MRI (best accuracy 78.90%), which not only demonstrates the potential for further research in this direction, but also indicates that the benchmark can serve as a foundation for data-constrained learning and clinical translation. Code and data will be released upon acceptance.