RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
arXiv:2607.05035v1 Announce Type: cross Abstract: Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation. This study proposes RUFNet, a Hybrid Mamba-based few-shot framework that combines support mask refinement with uncertainty-aware posterior fusion. To preserve support-query dependencies with manageable cost, RUFNet adopts a Hybrid Mamba interaction backbone with linear complexity. To reduce support-mask noise, an Attention-Guided Mask Refinement module (AGM
The continuous advancements in AI and deep learning research, particularly in medical imaging, drive the persistent efforts to improve diagnostic accuracy and efficiency.
Improved few-shot learning for medical image segmentation can lead to more accessible and accurate AI-assisted diagnostics, especially in rare diseases or resource-constrained settings.
The ability to perform robust brain tumor segmentation with limited data could accelerate the development and deployment of AI tools in clinical environments, reducing the need for vast labeled datasets.
- · Medical AI developers
- · Healthcare providers
- · Patients with complex conditions
- · Medical research institutions
- · Traditional manual image analysis methods
More precise and faster brain tumor diagnoses become possible using AI with less data.
The cost and time associated with developing specialized medical AI models decrease, expanding their adoption.
This could lead to a proliferation of AI diagnostics across various medical fields, democratizing advanced medical imaging interpretation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI