Bayesian Neural Networks for 2D MRI Segmentation
This addresses the need for confidence estimation in deep learning-based medical imaging, which is crucial for safety-critical applications like pathology screening, but it appears incremental as it combines existing methods.
The paper tackles the problem of uncertainty quantification in medical image segmentation by introducing BA U-Net, which integrates Bayesian Neural Networks with Attention Mechanisms, achieving accurate and interpretable results on the BraTS 2020 dataset.
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.