Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation

By Yongchan Kwon, Joong-Ho Won, Beomjoon Kim, Myunghee Cho Paik. 2018

108 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. William M. Wells
2. Alireza Mehrtash
3. Clare M. Tempany
4. Purang Abolmaesumi
5. Tina Kapur
6. Philipp Oberdiek
7. Zhipeng Ding
8. Ce Liu

Citations in the corpus

• A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference (2019, background): “This quantity can be decomposed into the aleatoric and epistemic uncertainty [30, 36].”
• Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation (2019, methodology): “Using similar approaches for uncertainty estimation, Kwon et al. [26] and Sedai et al. [27] used Bayesian NNs for uncertainty quantification in segmentation of ischemic stroke lesions and visualization of retinal layers, respectively.”