Uncertainty in Medical Image Analysis
| DATE | First Author | Title | Publication |
|---|---|---|---|
| 20200624 | Florian Wenzel | Hyperparameter Ensembles for Robustness and Uncertainty Quantification (arxiv) | TBA |
| 20200610 | Miguel Monteiro | Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty (arxiv) (pytorch) | TBA |
| 20191001 | Charles Corbière | Addressing Failure Prediction by Learning Model Confidence (arxiv) (pytorch) | NeurlPS 2019 |
| 20190821 | Janis Postels | Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation (arxiv) (keras) | ICCV 2019 |
| 20190725 | Zach Eaton-Rosen | As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging (arxiv) | MICCAI 2019 |
| 201906 | Fredrik K. Gustafsson | Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision (arxiv) (project) | TBD |
| 20190606 | Yaniv Ovadia | Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (arxiv) | NeurlPS 2019 |
| 201905 | Simon Kohl | A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities (arxiv) | TBD |
| 201806 | Zach Eaton-Rosen | Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions (arxiv) | MICCAI 2018 |
| 201806 | Simon Kohl | A Probabilistic U-Net for Segmentation of Ambiguous Images (arxiv) (spotlight) (tf) (re-pytorch) (YouTube) | NeurIPS 2018 |
| 201703 | Alex Kendall | What uncertainties do we need in Bayesian deep learning for computer vision? (arxiv) | NeurIPS 2017 |
| 201612 | Balaji Lakshminarayanan | Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles (arxiv) | NeurIPS 2017 |
| 20161201 | Christian Rupprecht | (M-Heads) Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses (arxiv) | ICCV 2017 |
| 201511 | Alex Kendall | Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding (arxiv) | |
| 201506 | Yarin Gal | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (arxiv) | PMLR |
| DATE | First Author | Title | Publication |
|---|---|---|---|
| 20200627 | Yingda Xia | Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation | MedIA |
| 201909 | Yuta Hiasa | Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling | TMI |
| 20190728 | Yongchan Kwon | Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation | Computational Statistics & Data Analysis |
| 20190715 | Davood Karimi | Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images | Medical Image Analysis |
| 20190707 | Alain Jungo | Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation (arxiv) (pytorch) | MICCAI 2019 |
| 20190703 | Shi Hu | Supervised Uncertainty Quantification for Segmentation with Multiple Annotations (arxiv) | MICCAI 2019 |
| 20190618 | Florin C. Ghesu | Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment (arxiv) | MICCAI 2019 |
| 20190607 | Christian F. Baumgartner | PHiSeg: Capturing Uncertainty in Medical Image Segmentation (arxiv) (code) | MICCAI 2019 |
| 20190605 | Roger D. Soberanis-Mukul | An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation (arxiv) | TBD |
| 20190529 | Philipp Seeböck | Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT (arxiv) | TMI |
| 20190529 | Maithra Raghu | Direct Uncertainty Prediction for Medical Second Opinions (arxiv) (blog) | ICML 2019 |
| 20190522 | Rohit Jena | A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration | IPMI 2019 |
| 201903 | Huitong Pan | Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure (arxiv) | ISBI 2019 |
| 201902 | Guotai wang | Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks | Neurocomputing |
| 201901 | José Ignacio Orlando | U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans (arxiv) (Talk Slides) | ISBI 2019 |
| 20180826 | Leo Joskowicz | Automatic segmentation variability estimation with segmentation priors (Talk Slides) | Medical Image Analysis |
| 20180803 | Tanya Nair | Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation (arxiv) (tf) (Talk slides) (YouTube) | MICCAI 2018 & Medical Image Analysis |
| 201807 | Terrance DeVries | Leveraging Uncertainty Estimates for Predicting Segmentation Quality (arxiv) | TBD |
| 201806 | Felix Bragman | Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning (arxiv) (project) | MICCAI 2018 |
| 20180419 | Abhijit Guha Roy | Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling (arxiv) (online segmentation tool) | MICCAI 2018 Neurolimaging extention |
| 20180411 | Murat Seckin Ayhan | Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks | MIDL 2018 |
| 20171219 | Christian Leibig | Leveraging uncertainty information from deep neural networks for disease detection | Scientific Reports |
| 20171201 | Onur Ozdemir | Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection (arxiv) | NIPS 2017 Bayesian Deep Learning workshop |
| 201703 | Ryutaro Tanno | Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution (arxiv) | MICCAI 2017 |