mlsanityData to 7 Oct 2026

Bayesian Neural Networks for 2D MRI Segmentation

arXiv 2311.14875 ↗First posted 24 Nov 2023eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 24 Nov 2023.

Builds on

19 of 28 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  9. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  10. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  11. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  12. 2018Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batchescited by 133
7 more
  1. 2017TensorFlow Distributionscited by 105
  2. 2019Bi-Directional ConvLSTM U-Net with Densley Connected Convolutionscited by 82
  3. 2020SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentationcited by 70
  4. 2018Bayesian Layers: A Module for Neural Network Uncertaintycited by 57
  5. 2021Is MC Dropout Bayesian?cited by 35
  6. 2020Uncertainty Quantification using Variational Inference for Biomedical Image Segmentationcited by 10
  7. 2020MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contextscited by 4

Built on by

Not cited yet.