RUFNet: Brain tumor segmentation with Hybrid Mamba and few-shot learning

Accurate segmentation of brain tumors in magnetic resonance images is a key challenge in computer-aided diagnosis. When labeled data is scarce, few-shot learning techniques become a promising alternative. However, inter-patient variability, noisy support masks, and the lack of pixel-level uncertainty estimates limit their clinical application. Recent research proposes hybrid architectures that combine attention mechanisms with Mamba-type state space models to handle long-range dependencies with linear cost. A notable example is RUFNet, which integrates attention-guided mask refinement and uncertainty-aware posterior fusion, achieving Dice coefficients above 84% on the BraTS 2020 dataset for single-shot configuration. This approach not only improves prototype consistency but also adaptively weights predictions with prior information aligned to the query image. Behind these advances are real needs in hospital environments, where the integration of artificial intelligence must be carried out on robust and customized platforms. For example, a company like Q2BSTUDIO develops custom software that allows diagnostic centers to incorporate these segmentation techniques without relying on generic solutions. Furthermore, the scalability and security of these systems benefit from AWS and Azure cloud services, while result monitoring can be enriched with Power BI dashboards integrated into business intelligence workflows. Process automation through AI agents facilitates the review of multiple studies in parallel, always under strict cybersecurity policies. Ultimately, the combination of advanced models like RUFNet with custom applications and a robust cloud infrastructure paves the way toward more accessible and reliable precision oncology.