Improving the Reliability of Semantic Segmentation of Medical Images by
Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning
- URL: http://arxiv.org/abs/2108.11693v1
- Date: Thu, 26 Aug 2021 10:24:02 GMT
- Title: Improving the Reliability of Semantic Segmentation of Medical Images by
Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning
- Authors: Sora Iwamoto, Bisser Raytchev, Toru Tamaki and Kazufumi Kaneda
- Abstract summary: We propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning.
We show in the concrete setting of a semantic segmentation task that the proposed system is able to increase significantly the reliability of the model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper we propose a novel method which leverages the uncertainty
measures provided by Bayesian deep networks through curriculum learning so that
the uncertainty estimates are fed back to the system to resample the training
data more densely in areas where uncertainty is high. We show in the concrete
setting of a semantic segmentation task (iPS cell colony segmentation) that the
proposed system is able to increase significantly the reliability of the model.
Related papers
- Neural State-Space Models: Empirical Evaluation of Uncertainty
Quantification [0.0]
This paper presents preliminary results on uncertainty quantification for system identification with neural state-space models.
We frame the learning problem in a Bayesian probabilistic setting and obtain posterior distributions for the neural network's weights and outputs.
Based on the posterior, we construct credible intervals on the outputs and define a surprise index which can effectively diagnose usage of the model in a potentially dangerous out-of-distribution regime.
arXiv Detail & Related papers (2023-04-13T08:57:33Z) - Uncertainty Estimation by Fisher Information-based Evidential Deep
Learning [61.94125052118442]
Uncertainty estimation is a key factor that makes deep learning reliable in practical applications.
We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL)
In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
arXiv Detail & Related papers (2023-03-03T16:12:59Z) - Robust Deep Learning for Autonomous Driving [0.0]
We introduce a new criterion to reliably estimate model confidence: the true class probability ( TCP)
Since the true class is by essence unknown at test time, we propose to learn TCP criterion from data with an auxiliary model, introducing a specific learning scheme adapted to this context.
We tackle the challenge of jointly detecting misclassification and out-of-distributions samples by introducing a new uncertainty measure based on evidential models and defined on the simplex.
arXiv Detail & Related papers (2022-11-14T22:07:11Z) - BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen
Neural Networks [50.15201777970128]
We propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation.
BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset.
We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures.
arXiv Detail & Related papers (2022-07-14T12:50:09Z) - On the Practicality of Deterministic Epistemic Uncertainty [106.06571981780591]
deterministic uncertainty methods (DUMs) achieve strong performance on detecting out-of-distribution data.
It remains unclear whether DUMs are well calibrated and can seamlessly scale to real-world applications.
arXiv Detail & Related papers (2021-07-01T17:59:07Z) - Learning Uncertainty For Safety-Oriented Semantic Segmentation In
Autonomous Driving [77.39239190539871]
We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving.
We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function.
We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
arXiv Detail & Related papers (2021-05-28T09:23:05Z) - Learning Calibrated Uncertainties for Domain Shift: A Distributionally
Robust Learning Approach [150.8920602230832]
We propose a framework for learning calibrated uncertainties under domain shifts.
In particular, the density ratio estimation reflects the closeness of a target (test) sample to the source (training) distribution.
We show that our proposed method generates calibrated uncertainties that benefit downstream tasks.
arXiv Detail & Related papers (2020-10-08T02:10:54Z) - Probabilistic Deep Learning for Instance Segmentation [9.62543698736491]
We propose a generic method to obtain model-inherent uncertainty estimates within proposal-free instance segmentation models.
We evaluate our method on the BBBC010 C. elegans dataset, where it yields competitive performance.
arXiv Detail & Related papers (2020-08-24T19:51:48Z) - Unlabelled Data Improves Bayesian Uncertainty Calibration under
Covariate Shift [100.52588638477862]
We develop an approximate Bayesian inference scheme based on posterior regularisation.
We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
arXiv Detail & Related papers (2020-06-26T13:50:19Z) - Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using
Multi-Headed Auxiliary Networks [23.100727871427367]
We show that traditional training procedures for Neural Linear Models drastically underestimate uncertainty on out-of-distribution inputs.
We propose a novel training framework that captures useful predictive uncertainties for downstream tasks.
arXiv Detail & Related papers (2020-06-21T02:46:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.