Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 26 Aug 2021.
Builds on
6 of 15 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2019DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networkscited by 92
- 2018Leveraging Uncertainty Estimates for Predicting Segmentation Qualitycited by 44
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Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 1.
6 papers.
- 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
- 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
- 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
- 2022Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentationcited by 1
- 2024Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentationcited by 1
- 2025Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imagingcited by 0
- 2025Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging29 Jul 2025
- 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation25 Nov 2024
- 2024Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation5 Sep 2024
- 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods9 Oct 2023
- 2022Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation1 Nov 2022
- 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis5 Oct 2022