Towards safe deep learning: accurately quantifying biomarker uncertainty
in neural network predictions

Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions

22 June 2018

Zach Eaton-Rosen

Felix J. S. Bragman

Sotirios Bisdas

Sebastien Ourselin

M. Jorge Cardoso

Papers citing "Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions"

11 / 11 papers shown

Title
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation M. Valiuddin R. V. Sloun C.G.A. Viviers Peter H. N. de With Fons van der Sommen UQCV 89 1 0 25 Nov 2024
Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network Tianyu Xiong Skylar W. Wurster Hanqi Guo Tom Peterka Han-Wei Shen UQCV 48 1 0 26 Jul 2024
Topology-Aware Uncertainty for Image Segmentation Saumya Gupta Yikai Zhang Xiaoling Hu Prateek Prasanna Chao Chen 28 27 0 09 Jun 2023
A Review of Uncertainty Estimation and its Application in Medical Imaging K. Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang H. Fu UQCV 38 85 0 16 Feb 2023
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection Ishaan Bhat J. Pluim M. Viergever Hugo J. Kuijf MedIm 19 4 0 22 Jun 2022
On the relationship between calibrated predictors and unbiased volume estimation Teodora Popordanoska J. Bertels Dirk Vandermeulen F. Maes Matthew B. Blaschko 34 11 0 23 Dec 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 30 1,109 0 07 Jul 2021
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling Yuta Hiasa Y. Otake Masaki Takao Takeshi Ogawa Nobuhiko Sugano Yoshinobu Sato 15 109 0 21 Jul 2019
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation Alain Jungo M. Reyes UQCV 27 134 0 07 Jul 2019
Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter Petteri Teikari Raymond P. Najjar L. Schmetterer D. Milea MedIm 6 27 0 13 Oct 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015