Bayesian Neural Networks for 2D MRI Segmentation
Uncertainty quantification is vital for safety-critical Deep Learning applications like medical image segmentation. We introduce BA U-Net, an uncertainty-aware model for MRI segmentation that integrates Bayesian Neural Networks with Attention Mechanisms. BA U-Net delivers accurate, interpretable results, crucial for reliable pathology screening. Evaluated on BraTS 2020, this model addresses the critical need for confidence estimation in deep learning-based medical imaging.
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Deep LearningImage SegmentationMedical Image SegmentationMRI segmentationSegmentationSemantic SegmentationUncertainty QuantificationMethods 이 논문이 사용한 방법론
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