Uncertainty in Medical Image Analysis
| DATE | Task | First Author | Title | Publication |
|---|---|---|---|---|
| 201906 | All | Fredrik K. Gustafsson | Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision (arxiv) (project) | TBD |
| 20190606 | Class. | Yaniv Ovadia | Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (arxiv) | TBD |
| 2018 | Regression | MattiasTeye, HosseinAzizpour | Bayesian Uncertainty Estimation for Batch Normalized Deep Networks | ICML 2018 |
| 201703 | Class & Regression | Alex Kendall | What uncertainties do we need in Bayesian deep learning for computer vision? (arxiv) | NIPS 2017 |
| 201612 | Class | Balaji Lakshminarayanan | Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles (arxiv) | NerulIPS 2017 |
| 201511 | Seg. | Alex Kendall | Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding (arxiv) | |
| 201506 | Classification | Yarin Gal | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (arxiv) | PMLR |
| DATE | Task | First Author | Title | Publication |
|---|---|---|---|---|
| 20190728 | Seg. | Yongchan Kwon | Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation | Computational Statistics & Data Analysis |
| 20190618 | Classification | Florin C. Ghesu1 | Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment (arxiv) | MICCAI 2019 |
| 20190607 | Seg. | Christian F. Baumgartner | PHiSeg: Capturing Uncertainty in Medical Image Segmentation (arxiv) (code) | MICCAI 2019 |
| 20190605 | Seg. | Roger D. Soberanis-Mukul | An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation (arxiv) | - |
| 20190529 | Seg. | Philipp Seeböck | Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT (arxiv) | TMI |
| 20190529 | Classification | MaithraRaghu | Direct Uncertainty Prediction for Medical Second Opinions (arxiv) | - |
| 201905 | Seg. | Rohit Jena | A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration | IPMI 2019 |
| 201905 | Seg. | Suyash P. Awate | Estimating Uncertainty in MRF-based Image Segmentation: A Perfect-MCMC Approach | Medical Image Analysis |
| 201903 | Seg. | Huitong Pan | Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure(arxiv) | ISBI 2019 |
| 201902 | Seg. | Guotai wang | Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks | Neurocomputing |
| 201901 | Seg. | José Ignacio Orlando | U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans (arxiv) | ISBI 2019 |
| 201812 | motion interpolation | Bojan Kocev | Uncertainty-aware asynchronous scattered motion interpolation using Gaussian process regression | Computerized Medical Imaging and Graphics |
| 201808 | Seg. | Tanya Nair | Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation (arxiv) (code) | MICCAI 2018 |
| 201807 | Seg. | Terrance DeVries | Leveraging Uncertainty Estimates for Predicting Segmentation Quality (arxiv) | |
| 201806 | synthetic CT | Felix Bragman | Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning (arxiv) (project) | MICCAI 2018 |
| 20180419 | Seg. | Abhijit Guha Roy | Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling (arxiv) (online segmentation tool) | MICCAI 2018 Neurolimaging extention |
| 20180411 | Seg. | Murat Seckin Ayhan | Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks | MIDL 2018 |
| 201712 | Detection | Christian Leibig | Leveraging uncertainty information from deep neural networks for disease detection | Scientific Reports |
| 20171201 | Detection | 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 | Super-Resolution | Ryutaro Tanno | Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution | (arxiv) |
| 201712 | (arxiv) | |||
| 201 | (arxiv) |