To develop a unified framework for pixel-level segmentation and image-level classification of brain tumors in MRI under limited samples and labels.
Key Findings:
DARE-FUSE demonstrates leading performance on BraTS segmentation benchmarks and various classification datasets.
Ablation studies confirm that the framework achieves complementary gains with reduced pixel annotations.
Uncertainty maps and continuous priors provide interpretable decision support for surgical and radiotherapy applications.
Interpretation:
The DARE-FUSE framework effectively addresses challenges in MRI segmentation and classification of brain tumors, particularly in scenarios with limited data.
Limitations:
The framework's performance may vary with different MRI sequences and artifacts.
Dependence on high-quality input data for optimal results.
Conclusion:
DARE-FUSE offers a robust solution for MRI segmentation and classification of brain tumors, enhancing interpretability and decision-making in clinical settings.
A VHA study across 11 vendors finds AI-generated primary care notes score lower than clinician-written notes, with the largest deficits in thoroughness, organization, and usefulness
