Bayesian Neural Networks for 2D MRI Segmentation

Lohith Konathala
11/24/2023
eess.IVcs.CV

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Abstract

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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Cite this paper

@article{konathala2023bayesian,
  title  = {Bayesian Neural Networks for 2D MRI Segmentation},
  author = {Lohith Konathala},
  year   = {2023},
  eprint = {2311.14875},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.14875v3}
}

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