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

22 June 2018

Sebastien Ourselin

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

20 / 20 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 96 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 61 1 0 26 Jul 2024
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods Ling Huang S. Ruan Yucheng Xing Mengling Feng 46 20 0 09 Oct 2023
Topology-Aware Uncertainty for Image Segmentation Saumya Gupta Yikai Zhang Xiaoling Hu Prateek Prasanna Chao Chen 33 27 0 09 Jun 2023
Self-training with dual uncertainty for semi-supervised medical image segmentation Zhanhong Qiu Haitao Gan Mingzhi Shi Zhongwei Huang Zhi Yang 11 2 0 10 Apr 2023
A Review of Uncertainty Estimation and its Application in Medical Imaging K. Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang Huazhu Fu UQCV 54 87 0 16 Feb 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis Benjamin Lambert Florence Forbes A. Tucholka Senan Doyle Harmonie Dehaene M. Dojat 34 81 0 05 Oct 2022
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection Ishaan Bhat J. Pluim M. Viergever Hugo J. Kuijf MedIm 26 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 42 11 0 23 Dec 2021
Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows Elena Williams Sebastian Niehaus J. Reinelt A. Merola P. Mihai ... Evelyn Medawar Daniel Lichterfeld Ingo Roeder N. Scherf Maria del C. Valdés Hernández 31 3 0 06 Dec 2021
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation Yinghuan Shi Jian Zhang T. Ling Jiwen Lu Yefeng Zheng Qian Yu Lei Qi Yang Gao UQCV 19 144 0 17 Oct 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 66 1,112 0 07 Jul 2021
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples Ömer Faruk Tuna Ferhat Ozgur Catak M. T. Eskil AAML 27 32 0 08 Feb 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 67 100 0 11 Jan 2021
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment Florin-Cristian Ghesu Bogdan Georgescu Awais Mansoor Y. Yoo Eli Gibson ... Ramandeep Singh S. Digumarthy Mannudeep K. Kalra Sasa Grbic Dorin Comaniciu UQCV EDL 23 55 0 08 Jul 2020
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 21 110 0 21 Jul 2019
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation Alain Jungo M. Reyes UQCV 30 134 0 07 Jul 2019
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation Guotai Wang Wenqi Li Sebastien Ourselin Tom Kamiel Magda Vercauteren 19 151 0 18 Oct 2018
Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter Petteri Teikari Raymond P. Najjar L. Schmetterer D. Milea MedIm 27 27 0 13 Oct 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015