Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
暂无分享,去创建一个
Purang Abolmaesumi | William M. Wells | Alireza Mehrtash | Clare M. Tempany | Tina Kapur | C. Tempany | T. Kapur | W. Wells | P. Abolmaesumi | Alireza Mehrtash
[1] Joseph Keshet,et al. Out-of-Distribution Detection using Multiple Semantic Label Representations , 2018, NeurIPS.
[2] Matthias Rottmann,et al. Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[3] Nico Karssemeijer,et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.
[4] Dwarikanath Mahapatra,et al. Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning , 2018, COMPAY/OMIA@MICCAI.
[5] Matthew B. Blaschko,et al. The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Sébastien Ourselin,et al. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks , 2017, BrainLes@MICCAI.
[7] H. Rolf Jäger,et al. Let's agree to disagree: learning highly debatable multirater labelling , 2019, MICCAI.
[8] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[9] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[10] Mauricio Reyes,et al. Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation , 2019, MICCAI.
[11] Roberto Cipolla,et al. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.
[12] Eunho Yang,et al. Uncertainty-Aware Attention for Reliable Interpretation and Prediction , 2018, NeurIPS.
[13] Klaus H. Maier-Hein,et al. A Probabilistic U-Net for Segmentation of Ambiguous Images , 2018, NeurIPS.
[14] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .
[15] Purang Abolmaesumi,et al. Automatic high resolution segmentation of the prostate from multi-planar MRI , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[16] Christos Davatzikos,et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.
[17] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[18] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[19] Kilian M. Pohl,et al. Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models , 2015, SIAM J. Imaging Sci..
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[22] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[23] Milos Hauskrecht,et al. Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.
[24] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[25] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[26] Graham W. Taylor,et al. Learning Confidence for Out-of-Distribution Detection in Neural Networks , 2018, ArXiv.
[27] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[28] Max A. Viergever,et al. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images , 2017, STACOM@MICCAI.
[29] Nico Karssemeijer,et al. Computer-Aided Detection of Prostate Cancer in MRI , 2014, IEEE Transactions on Medical Imaging.
[30] Georg Heinze,et al. Medical Biostatistics I , 2010 .
[31] Jelmer M. Wolterink,et al. Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI , 2018, Medical Imaging: Image Processing.
[32] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[33] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[34] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[35] Paul Suetens,et al. Optimization with soft Dice can lead to a volumetric bias , 2019, BrainLes@MICCAI.
[36] Purang Abolmaesumi,et al. On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment , 2019, IEEE Transactions on Medical Imaging.
[37] Konstantinos Kamnitsas,et al. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.
[38] Swami Sankaranarayanan,et al. Learning From Noisy Labels by Regularized Estimation of Annotator Confusion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Sébastien Ourselin,et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.
[40] Simon Andermatt,et al. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge , 2019, IEEE Transactions on Medical Imaging.
[41] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[42] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Purang Abolmaesumi,et al. Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images , 2019, Medical Image Anal..
[44] Siegfried Wahl,et al. Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.
[45] Myunghee Cho Paik,et al. Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation , 2018 .
[46] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[47] Kevin Smith,et al. Bayesian Uncertainty Estimation for Batch Normalized Deep Networks , 2018, ICML.
[48] Kibok Lee,et al. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples , 2017, ICLR.
[49] Kemal Tuncali,et al. Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy , 2019, IEEE Transactions on Medical Imaging.
[50] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[51] Henkjan Huisman,et al. Supervised Uncertainty Quantification for Segmentation with Multiple Annotations , 2019, MICCAI.
[52] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[53] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[54] Florian Jung,et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..
[55] Ender Konukoglu,et al. PHiSeg: Capturing Uncertainty in Medical Image Segmentation , 2019, MICCAI.
[56] Bohyung Han,et al. Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.