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https://github.com/junma11/meduncertainty

Uncertainty in Medical Image Analysis
https://github.com/junma11/meduncertainty
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Uncertainty in Medical Image Analysis
Host: GitHub
URL: https://github.com/junma11/meduncertainty
Owner: JunMa11
Created: 2019-06-13T14:59:19.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-11-06T19:40:01.000Z (about 3 years ago)
Last Synced: 2023-11-07T17:24:24.504Z (about 1 year ago)
Size: 23.4 KB
Stars: 303
Watchers: 18
Forks: 42
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        # MedUncertainty

Uncertainty in Medical Image Analysis

## Benchmarks

- [QUBIQ](https://qubiq21.grand-challenge.org/)

- [BraTS 2020 Task 3](https://www.med.upenn.edu/cbica/brats2020/tasks.html)

## Uncertainty Estimation Methods

|DATE|First Author|Title|Publication|

|---|---|------------|---|

|20200624|Florian Wenzel|Hyperparameter Ensembles for Robustness and Uncertainty Quantification [(arxiv)](https://arxiv.org/abs/2006.13570)|TBA|

|20200610|[Miguel Monteiro](https://scholar.google.com/citations?user=LyabfXcAAAAJ&hl=en)|Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty [(arxiv)](https://arxiv.org/abs/2006.06015) [(pytorch)](https://github.com/biomedia-mira/stochastic_segmentation_networks)|TBA|

|20191001|Charles Corbière|Addressing Failure Prediction by Learning Model Confidence [(arxiv)](https://arxiv.org/pdf/1910.04851.pdf) [(pytorch)](https://github.com/valeoai/ConfidNet)|[NeurlPS 2019](https://nips.cc/Conferences/2019/AcceptedPapersInitial)|

|20190821|[Janis Postels](https://scholar.google.com/citations?user=_z8NnVsAAAAJ&hl=en&oi=ao)|Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation [(arxiv)](https://arxiv.org/pdf/1908.00598.pdf) [(keras)](https://github.com/janisgp/Sampling-free-Epistemic-Uncertainty)|ICCV 2019|

|20190725|Zach Eaton-Rosen|As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging [(arxiv)](https://arxiv.org/abs/1907.11555)|MICCAI 2019|

|201906|Fredrik K. Gustafsson|Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision [(arxiv)](https://arxiv.org/abs/1906.01620) [(project)](http://www.fregu856.com/publication/evaluating_bdl/)|TBD|

|20190606|[Yaniv Ovadia](https://scholar.google.com/citations?hl=en&user=POp8_IsAAAAJ&view_op=list_works&sortby=pubdate)|Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift [(arxiv)](https://arxiv.org/abs/1906.02530)|[NeurlPS 2019](https://nips.cc/Conferences/2019/AcceptedPapersInitial)|

|201905|[Simon Kohl](https://www.simonkohl.com/)|A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities [(arxiv)](https://arxiv.org/pdf/1905.13077.pdf)|TBD|

|201806|Zach Eaton-Rosen|Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions [(arxiv)](https://arxiv.org/abs/1806.08640)|MICCAI 2018|

|201806|[Simon Kohl](https://www.simonkohl.com/)|A Probabilistic U-Net for Segmentation of Ambiguous Images [(arxiv)](https://arxiv.org/pdf/1806.05034.pdf) [(spotlight)](https://neurips.cc/media/Slides/nips/2018/220e(05-09-45)-05-10-35-12641-A_Probabilistic.pdf) [(tf)](https://github.com/SimonKohl/probabilistic_unet) [(re-pytorch)](https://github.com/stefanknegt/Probabilistic-Unet-Pytorch) [(YouTube)](https://www.youtube.com/watch?v=-cfFxQWfFrA&feature=youtu.be)|[NeurIPS 2018](https://nips.cc/Conferences/2018/Schedule?showEvent=12641)|

|201703|[Alex Kendall](https://scholar.google.com/citations?user=hE2mTp4AAAAJ&hl=en&oi=sra)|What uncertainties do we need in Bayesian deep learning for computer vision? [(arxiv)](https://arxiv.org/abs/1703.04977)|[NeurIPS 2017](https://dl.acm.org/citation.cfm?id=3295309)|

|201612|[Balaji Lakshminarayanan](https://scholar.google.com/citations?user=QYn8RbgAAAAJ&hl=en&oi=sra)|Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [(arxiv)](https://arxiv.org/abs/1612.01474)|[NeurIPS 2017](http://papers.nips.cc/paper/7219-simple-and-scalable-predictive-uncertainty-estimation-using-deep-ensembles)|

|20161201|[Christian Rupprecht](https://chrirupp.github.io/)|**(M-Heads)** Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses [(arxiv)](https://arxiv.org/pdf/1612.00197.pdf)|[ICCV 2017](http://openaccess.thecvf.com/content_ICCV_2017/papers/Rupprecht_Learning_in_an_ICCV_2017_paper.pdf)|

|201511|[Alex Kendall](https://scholar.google.com/citations?user=hE2mTp4AAAAJ&hl=en&oi=sra)|Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding [(arxiv)](https://arxiv.org/abs/1511.02680)|

|201506|[Yarin Gal](https://scholar.google.com/citations?user=SIayDoQAAAAJ&hl=en&oi=sra)|Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning [(arxiv)](https://arxiv.org/abs/1506.02142)|[PMLR](http://proceedings.mlr.press/v48/gal16.html)|

## Uncertainty in Medical Image Analysis

|DATE|First Author|Title|Publication|

|---|---|-------|---|

|20200627|Yingda Xia|Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation|[MedIA](https://www.sciencedirect.com/science/article/pii/S1361841520301304)|

|201909|[Yuta Hiasa](https://scholar.google.com/citations?hl=en&user=_NrX-gUAAAAJ&view_op=list_works&sortby=pubdate)|Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling|[TMI](https://ieeexplore.ieee.org/document/8830493)|

|20190728|[Yongchan Kwon](https://scholar.google.com/citations?hl=en&user=PElI4ikAAAAJ&view_op=list_works&sortby=pubdate)|Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation|[Computational Statistics & Data Analysis](https://www.sciencedirect.com/science/article/pii/S016794731930163X)|

|20190715|Davood Karimi|Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images|[Medical Image Analysis](https://www.sciencedirect.com/science/article/pii/S1361841519300623)|

|20190707|[Alain Jungo](https://scholar.google.com.hk/citations?user=ur9i7Q4AAAAJ&hl=zh-CN&oi=sra)|Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation [(arxiv)](https://arxiv.org/pdf/1907.03338.pdf) [(pytorch)](https://github.com/alainjungo/reliability-challenges-uncertainty)|[MICCAI 2019](https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_6)|

|20190703|Shi Hu|Supervised Uncertainty Quantification for Segmentation with Multiple Annotations [(arxiv)](https://arxiv.org/pdf/1907.01949.pdf)|[MICCAI 2019](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_16)|

|20190618|[Florin C. Ghesu](https://scholar.google.ca/citations?hl=en&user=Z1-KZ8RoM6YC&view_op=list_works&sortby=pubdate)|Quantifying and Leveraging Classiﬁcation Uncertainty for Chest Radiograph Assessment [(arxiv)](https://arxiv.org/pdf/1906.07775.pdf) |[MICCAI 2019](https://link.springer.com/chapter/10.1007%2F978-3-030-32226-7_75)|

|20190607|Christian F. Baumgartner|PHiSeg: Capturing Uncertainty in Medical Image Segmentation [(arxiv)](https://arxiv.org/abs/1906.04045) [(code)](https://github.com/baumgach/PHiSeg-code)|[MICCAI 2019](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_14)|

|20190605|[Roger D. Soberanis-Mukul](https://scholar.google.com/citations?user=7soRur0AAAAJ&hl=en&oi=ao)|An Uncertainty-Driven GCN Reﬁnement Strategy for Organ Segmentation [(arxiv)](https://arxiv.org/pdf/1906.02191.pdf)|TBD|

|20190529|[Philipp Seeböck](https://optima.meduniwien.ac.at/about-us/team/computational-imaging-research/philipp-seeboeck/)|Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT [(arxiv)](https://arxiv.org/pdf/1905.12806.pdf)|[TMI](https://ieeexplore.ieee.org/abstract/document/8727461)|

|20190529|[Maithra Raghu](http://maithraraghu.com/)|Direct Uncertainty Prediction for Medical Second Opinions [(arxiv)](https://arxiv.org/pdf/1807.01771.pdf) [(blog)](http://maithraraghu.com/blog/2019/Direct_Uncertainty_Prediction/)|[ICML 2019](http://proceedings.mlr.press/v97/raghu19a.html)|

|20190522|[Rohit Jena](https://scholar.google.ca/citations?user=kZQQFE4AAAAJ&hl=en&oi=sra)|A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration|[IPMI 2019](https://link.springer.com/chapter/10.1007/978-3-030-20351-1_1)|

|201903|Huitong Pan|Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure [(arxiv)](https://arxiv.org/abs/1506.02142) |[ISBI 2019](https://ieeexplore.ieee.org/document/8759300)|

|201902|[Guotai wang](https://scholar.google.com/citations?user=Z2sFN4EAAAAJ&hl=en)|Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks|[Neurocomputing](https://www.sciencedirect.com/science/article/pii/S0925231219301961)|

|201901|[José Ignacio Orlando](https://scholar.google.com/citations?user=2N3oD28AAAAJ&hl=en&oi=sra)|U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans [(arxiv)](https://arxiv.org/abs/1901.07929) [(Talk Slides)](https://ignaciorlando.github.io/data/isbi-2019-presentation.pdf)|[ISBI 2019](https://ieeexplore.ieee.org/document/8759581)|

|20180826|[Leo Joskowicz](https://www.cse.huji.ac.il/~josko/)|Automatic segmentation variability estimation with segmentation priors [(Talk Slides)](https://labels.tue-image.nl/wp-content/uploads/2018/09/Keynote-Joskoxicz.pdf)|[Medical Image Analysis](https://www.sciencedirect.com/science/article/pii/S1361841518306546)|

|20180803|Tanya Nair|Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation [(arxiv)](https://arxiv.org/pdf/1808.01200.pdf) [(tf)](https://github.com/tanyanair/segmentation_uncertainty) [(Talk slides)](https://labels.tue-image.nl/wp-content/uploads/2018/09/Keynote-Arbel.pdf) [(YouTube)](https://www.youtube.com/watch?v=C9bdS7ehMNc)|[MICCAI 2018](https://link.springer.com/chapter/10.1007/978-3-030-00928-1_74) & [Medical Image Analysis](https://www.sciencedirect.com/science/article/pii/S1361841519300994)|

|201807|[Terrance DeVries](https://scholar.google.com/citations?user=VFPOOsoAAAAJ&hl=en&oi=sra)|Leveraging Uncertainty Estimates for Predicting Segmentation Quality [(arxiv)](https://arxiv.org/abs/1807.00502)|[TBD]()|

|201806|[Felix Bragman](https://scholar.google.com/citations?user=0Qn2UvAAAAAJ&hl=en&oi=sra)|Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning [(arxiv)](https://arxiv.org/abs/1806.06595) [(project)](https://rt416.github.io/publication/multitask-01/)|[MICCAI 2018](https://link.springer.com/chapter/10.1007/978-3-030-00937-3_1)|

|20180419|[Abhijit Guha Roy](https://scholar.google.com/citations?user=r2ulM_sAAAAJ&hl=en&oi=sra)|Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling [(arxiv)](https://arxiv.org/abs/1804.07046) [(online segmentation tool)](http://quicknat.ai-med.de/) |[MICCAI 2018](https://link.springer.com/chapter/10.1007/978-3-030-00928-1_75) [Neurolimaging extention](https://www.sciencedirect.com/science/article/pii/S1053811919302319?dgcid=author)|

|20180411|[Murat Seckin Ayhan](https://scholar.google.com/citations?user=ZX9sqrkAAAAJ&hl=en&oi=sra)|Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks |[MIDL 2018](https://openreview.net/forum?id=rJZz-knjz)|

|20171219|Christian Leibig|Leveraging uncertainty information from deep neural networks for disease detection |[Scientific Reports](https://www.nature.com/articles/s41598-017-17876-z)|

|20171201|[Onur Ozdemir](https://scholar.google.com/citations?user=SeYR1GwAAAAJ&hl=en&oi=sra)|Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection [(arxiv)](https://arxiv.org/pdf/1712.00497.pdf)|[NIPS 2017 Bayesian Deep Learning workshop](http://bayesiandeeplearning.org/2017/index.html)|

|201703|[Ryutaro Tanno](https://scholar.google.com/citations?user=NiEvNoEAAAAJ&hl=en&oi=sra)|Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution [(arxiv)](https://arxiv.org/pdf/1705.00664.pdf)|[MICCAI 2017](https://link.springer.com/chapter/10.1007/978-3-319-66182-7_70)|