Confidence calibration and predictive uncertainty estimation for deep medical image segmentation

Mehrtash, Alireza; Wells, William M.; Tempany, Clare M.; Abolmaesumi, Purang; Kapur, Tina

doi:10.1109/tmi.2020.3006437

Cited by 428Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Confidence calibration and predictive uncertainty estimation for deep medical image segmentation

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Mehrtash, Alireza, Wells, William M., Tempany, Clare M., Abolmaesumi, Purang, Kapur, Tina·2020·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2020.3006437PMC PMC7704933

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Abstract

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.

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In 2020, Natekar et al. [694] used DenseUnet, ResUnet, SimUnet with TTD (test time dropout) for VI for segmentation of brain tumor on 1 dataset.
“Natekar al. [694] | et | 2020 | DenseUnet, ResUnet, SimUnet | Segmentation | Brain tumor | 1 | TTD (test time dropout) for VI”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [694]
The Brain Tumor Segmentation Task section describes one BraTS dataset, and Experiments > MC dropout reports dropout sampling at inference time. However, Experiments > Training Baselines says the study used a U-Net-like FCN; our copy does not support the claim that it used DenseUnet, ResUnet, and SimUnet. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Disagreement among CV models is increasingly reused as a proxy for epistemic uncertainty in downstream applications such as quality control and human-in-the-loop review.
“Increasingly, disagreement among these CV models is reused as a proxy for epistemic uncertainty in downstream applications such as quality control and human-in-the-loop review [11,18,15].”
From Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation (2026) · cites this paper as [18]
The Contributions, Results and Discussion (page numbers unavailable) describe model ensembles whose predictive uncertainty helps assess segmentation quality and detect failures; the Introduction also proposes seeking human intervention rather than allowing a silent failure. But the paper uses entropy of ensemble predictions, not disagreement among models specifically as a proxy for epistemic uncertainty. It does not establish that this practice is increasingly reused across CV applications or document human-in-the-loop review workflows. (LLM, conf=0.84)
Experimental — accuracy not yet measured

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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Untitled work
(2014) · Cited by 33,419 · see citing works
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Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
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He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793 · see citing works
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016) · arXiv · doi:10.48550/arxiv.1612.01474 · Cited by 3,666 · see citing works
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Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
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Kendall, A., & Gal, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.04977 · Cited by 3,088 · see citing works
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Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917 · see citing works
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Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829 · see citing works
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Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1610.02136 · Cited by 1,968 · see citing works
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314 · see citing works
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Liang, S., Li, Y., & Srikant, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02690 · Cited by 1,117 · see citing works
Obtaining well calibrated probabilities using Bayesian binning
Pakdaman Naeini, M., Cooper, G., & Hauskrecht, M. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9602 · Cited by 991 · see citing works
Evaluation of prostate segmentation algorithms for mri: The promise12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works
Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510 · see citing works
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution SamplesPreprint
Lee, K., Lee, H., Lee, K., & Shin, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09325 · Cited by 509 · see citing works
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Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M. D., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75238-9_38 · Cited by 485 · see citing works
Computer-Aided Detection of Prostate Cancer in MRI
Litjens, G. J. S., Debats, O. A., Barentsz, J. O., Karssemeijer, N., & Huisman, H. J. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2303821 · Cited by 453 · see citing works
A probabilistic u-net for segmentation of ambiguous images
Kohl, S. A. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S. M. A., Rezende, D. J., & Ronneberger, O. (2018) · arXiv · doi:10.48550/arxiv.1806.05034 · Cited by 439 · see citing works
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DeVries, T., & Taylor, G. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04865 · Cited by 354 · see citing works
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Ghafoorian, M., Mehrtash, A., Kapur, T., Karssemeijer, N., Marchiori, E., Pesteie, M., Guttmann, C. R. G., de Leeuw, F., Tempany, C. M., van Ginneken, B., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_59 · Cited by 337 · see citing works
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Tanno, R., Saeedi, A., Sankaranarayanan, S., Alexander, D. C., & Silberman, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01150 · Cited by 258 · see citing works
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Jungo, A., & Reyes, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_6 · Cited by 156 · see citing works
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Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
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Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S. S., Spadinger, I., Abolmaesumi, P., & Salcudean, S. E. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.005 · Cited by 127 · see citing works
Self-citation
Supervised uncertainty quantification for segmentation with multiple annotations
Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., & Welling, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_16 · Cited by 105 · see citing works
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Seo, S., Seo, P. H., & Han, B. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00924 · Cited by 73 · see citing works
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Mehrtash, A., Ghafoorian, M., Pernelle, G., Ziaei, A., Heslinga, F. G., Tuncali, K., Fedorov, A., Kikinis, R., Tempany, C. M., Wells, W. M., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2876796 · Cited by 69 · see citing works
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Kwon, Y., Won, J., Kim, B. J., & Paik, M. C. (2018) · Cited by 67 · see citing works
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Shalev, G., Adi, Y., & Keshet, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06664 · Cited by 65 · see citing works
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Sander, J., Vos, B. D., Wolterink, J., & Išgum, I. (2018) · Image Processing · doi:10.1117/12.2511699 · Cited by 64 · see citing works
Medical Biostatistics
Indrayan, A. (2012) · Chapman & Hall/CRC eBooks · doi:10.1201/b12662 · Cited by 63 · see citing works
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Rottmann, M., & Schubert, M. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00176 · Cited by 32 · see citing works
Automatic high resolution segmentation of the prostate from multi-planar MRI
Meyer, A., Mehrtash, A., Rak, M., Schindele, D., Schostak, M., Tempany, C. M., Kapur, T., Abolmaesumi, P., Fedorov, A., & Hansen, C. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363549 · Cited by 29 · see citing works
Self-citation
Optimization with Soft Dice Can Lead to a Volumetric Bias
Bertels, J., Robben, D., Vandermeulen, D., & Suetens, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_9 · Cited by 27 · see citing works
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Niethammer, M., Pohl, K. M., Janoos, F., & Wells, W. M. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1058601 · Cited by 6 · see citing works
Self-citation
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“Negative log-likelihood (NLL), and Brier score [49], are both strictly proper scoring rules that have been previously used in several studies for evaluating predictive uncertainty [5], [18], [27].”
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Research works printed but not linked: 4

[4]MRBrainS18. (2018) Grand challenge on MR brain segmentation at MICCAI 2018. [Online]. Available: https://mrbrains18.isi.uu.nl/
[23]Liao Z, “ On modelling label quality assessment,” arXiv preprint arXiv:1911.00674, 2019. · arXiv
[54]Berman M, Rannen Triki A, and Blaschko MB, “ The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4413– 4421.
[57]Sudre CH, Anson BG, Ingala S, Lane CD, Jimenez D, Haider L, Varsavsky T, Tanno R, Smith L, Ourselin S, “ Lets agree to disagree: Learning highly debatable multirater labelling,” in International Conference on Medical Image Computing and Computer-Assisted Intervention Springer, 2019, pp. 665– 673.

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Gros, C., Lemay, A., & Cohen‐Adad, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102038 · Cited by 80 · see citing works
Deep learning prediction models based on EHR trajectories: A systematic review
Amirahmadi, A., Ohlsson, M., & Etminani, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104430 · Cited by 87 · see citing works
MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images
Zimmerer, D., Full, P. M., Isensee, F., Jäger, P. F., Adler, T. J., Petersen, J., Koehler, G., Roß, T., Reinke, A., Kascenas, A., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3170077 · Cited by 68 · see citing works
Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
Ng, M., Guo, F., Biswas, L., Petersen, S. E., Piechnik, S., Neubauer, S., & Wright, G. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3232730 · Cited by 59 · see citing works
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Li, H., Yang, N., Ser, J. D., & Yang, G. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08016-4 · Cited by 65 · see citing works
End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT
Nowak, S., Theis, M., Wichtmann, B. D., Faron, A., Froelich, M. F., Tollens, F., Geißler, H. L., Block, W., Luetkens, J. A., Attenberger, U. I., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08313-x · Cited by 64 · see citing works
Improving Calibration and Out-of-Distribution Detection in Deep Models for Medical Image Segmentation
Karimi, D., & Gholipour, A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3159510 · Cited by 73 · see citing works
On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images
Khalil, Y. A., Amirrajab, S., Cristian Lorenz, A., Weese, J., Pluim, J. P. W., & Breeuwer, M. M. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102688 · Cited by 63 · see citing works
Assessing the generalizability of artificial intelligence in radiology: a systematic review of performance across different clinical settings
Suleman, M. U., Mursaleen, M., Khalil, U., Saboor, A., Bilal, M., Khan, S. A., Subhani, M. A., Hussnain, M. A., Tabassum, S. N., & Tahir, M. R. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000004166 · Cited by 60 · see citing works
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
González, C., Gotkowski, K., Fuchs, M., Bucher, A. M., Dadras, A. A., Fischbach, R., Kaltenborn, I., & Mukhopadhyay, A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102596 · Cited by 59 · see citing works
Diabetic foot ulcers segmentation challenge report: Benchmark and analysis
Yap, M. H., Cassidy, B., Byra, M., Liao, T.-Y., Yi, H., Galdrán, A., Chen, Y.-H., Brüngel, R., Koitka, S., Friedrich, C. M., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103153 · Cited by 53 · see citing works
Evidence-based uncertainty-aware semi-supervised medical image segmentation
Chen, Y., Yang, Z., Shen, C., Wang, Z., Zhang, Z., Qin, Y., Wei, X., Lu, J., Liu, Y., & Zhang, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108004 · Cited by 52 · see citing works
Application of belief functions to medical image segmentation: A review
Huang, L., Ruan, S., & Denœux, T. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.008 · Cited by 49 · see citing works
The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer
Tapper, W., Carneiro, G., Mikropoulos, C., Thomas, S. A., Evans, P., & Boussios, S. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14030287 · Cited by 45 · see citing works
Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation
Camarasa, R., Bos, D., Hendrikse, J., Nederkoorn, P. J., Kooi, M. E., van der Lugt, A., & de Bruijne, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60365-6_4 · Cited by 45 · see citing works
D-LEMA: Deep Learning Ensembles from Multiple Annotations - Application to Skin Lesion Segmentation
Mirikharaji, Z., Abhishek, K., Izadi, S., & Hamarneh, G. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00203 · Cited by 43 · see citing works
Translation Consistent Semi-Supervised Segmentation for 3D Medical Images
Liu, Y., Tian, Y., Wang, C., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3468896 · Cited by 44 · see citing works
Confidence-Based Out-of-Distribution Detection: A Comparative Study and Analysis
Berger, C., Paschali, M., Glocker, B., & Kamnitsas, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87735-4_12 · Cited by 42 · see citing works
Understanding Calibration of Deep Neural Networks for Medical Image Classification
Sambyal, A., Niyaz, U., Krishnan, N. C., & Bathula, D. R. (2023) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2023.107816 · Cited by 45 · see citing works
Variance‐aware attention U‐Net for multi‐organ segmentation
Lin, H., Li, Z., Yang, Z., & Wang, Y. (2021) · Medical Physics · doi:10.1002/mp.15322 · Cited by 40 · see citing works
Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning
Leinonen, J., Hamann, U., & Germann, U. (2022) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0043.1 · Cited by 38 · see citing works
Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images
Dawood, T., Chen, C., Sidhu, B. S., Ruijsink, B., Gould, J., Porter, B., Elliott, M. K., Mehta, V. S., Rinaldi, C. A., Anton, E. P., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102861 · Cited by 45 · see citing works
MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans
Lyu, Y., & Tian, X. (2025) · Bioengineering · doi:10.3390/bioengineering12020140 · Cited by 38 · see citing works
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
Mehta, R., Christinck, T., Nair, T., Bussy, A., Premasiri, S., Costantino, M., Chakravarthy, M. M., Arnold, D. L., Gal, Y., & Arbel, T. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3114097 · Cited by 38 · see citing works
A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation
McCrindle, B. W., Zukotynski, K., Doyle, T. E., & Noseworthy, M. D. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210031 · Cited by 38 · see citing works
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
Galdrán, A., Carneiro, G., & Ballester, M. Á. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26354-5_4 · Cited by 36 · see citing works
Measurement Guidance in Diffusion Models: Insight from Medical Image Synthesis
Luo, Y., Yang, Q., Fan, Y., Qi, H., & Xia, M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3399098 · Cited by 35 · see citing works
A Survey of Computer Vision Technologies in Urban and Controlled-environment Agriculture
Luo, J., Li, B., & Leung, C. (2023) · ACM Computing Surveys · doi:10.1145/3626186 · Cited by 36 · see citing works
Uncertainty-driven trustworthy defect detection for high-resolution powder bed images in selective laser melting
Zhao, Z., Liu, W., Ren, J., Wang, C., He, Y., Zhang, X., & Chen, X. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.11.006 · Cited by 34 · see citing works
Boundary-wise loss for medical image segmentation based on fuzzy rough sets
Qiao, L., Chen, X., Chen, C., & Garibaldi, J. M. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120183 · Cited by 34 · see citing works
Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs
Pandey, P. U., Ballios, B. G., Christakis, P. G., Kaplan, A., Mathew, D. J., Tone, S. O., Wan, M. J., Micieli, J. A., & Wong, J. C. Y. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-322183 · Cited by 36 · see citing works
Artificial intelligence uncertainty quantification in radiotherapy applications − A scoping review
Wahid, K. A., Kaffey, Z. Y., Farris, D. P., Humbert‐Vidan, L., Moreno, A. C., Rasmussen, M. E., Ren, J., Naser, M. A., Netherton, T., Korreman, S. S., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110542 · Cited by 35 · see citing works
A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty
Qiao, L., Chen, X., Chen, C., & Garibaldi, J. M. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2022.3228332 · Cited by 32 · see citing works
Calibrating segmentation networks with margin-based label smoothing
Murugesan, B., Liu, B., Galdrán, A., Ayed, I. B., & Dolz, J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102826 · Cited by 33 · see citing works
Efficient Bayesian Uncertainty Estimation for nnU-Net
Zhao, Y., Yang, C., Schweidtmann, A. M., & Tao, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_51 · Cited by 31 · see citing works
Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression: Simplifying Complex T2F Calculations
Zare, A., Shoeibi, A., Bajestani, N. S., Moridian, P., Alizadehsani, R., Hallaji, M., & Khosravi, A. (2022) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2022.3148569 · Cited by 30 · see citing works
Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation
González, C., Gotkowski, K., Bucher, A. M., Fischbach, R., Kaltenborn, I., & Mukhopadhyay, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_29 · Cited by 30 · see citing works
ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
Hansen, S., Gautam, S., Salahuddin, S. A., Kampffmeyer, M., & Jenssen, R. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102870 · Cited by 29 · see citing works
Uncertainty Co-Estimator for Improving Semi-Supervised Medical Image Segmentation
Zeng, X. J., Xiong, S., Xu, J., Du, G., & Rong, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3570310 · Cited by 29 · see citing works
Calibrating Uncertainty for Semi-Supervised Crowd Counting
Chen, L., Hu, X., Abousamra, S., & Chen, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01534 · Cited by 28 · see citing works
Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning
Yuan, Y., Wang, X., Yang, X., & Heng, P. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3484166 · Cited by 29 · see citing works
Improving cross-domain generalizability of medical image segmentation using uncertainty and shape-aware continual test-time domain adaptation
Zhu, J., Bolsterlee, B., Song, Y., & Meijering, E. H. W. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103422 · Cited by 29 · see citing works
Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals
Lee, H., Choi, Y. R., Lee, H. K., Jeong, J., Hong, J., Shin, H., & Kim, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01378-0 · Cited by 27 · see citing works
Automated Ensemble for Deep Learning Inference on Edge Computing Platforms
Bai, Y., Chen, L., Abdel-Mottaleb, M. S., & Xu, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3102945 · Cited by 27 · see citing works
On Calibrating Semantic Segmentation Models: Analyses and An Algorithm
Wang, D., Gong, B., & Wang, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02265 · Cited by 27 · see citing works
Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix
Qiao, P., Li, H., Song, G., Han, H., Gao, Z., Tian, Y., Liang, Y., Li, X., Zhou, S. K., & Chen, J. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3232572 · Cited by 26 · see citing works
Neuroinfinity: A Lightweight Prompt-Driven Vision–Language Framework for Alzheimer’s Disease Stage Classification Using Magnetic Resonance Imaging
Kaushik, P., Kukreja, V., Jain, E., Ati, M., & Hariharan, S. (2025) · IEEE Access · doi:10.1109/access.2025.3630141 · Cited by 25 · see citing works
Post Training Uncertainty Calibration Of Deep Networks For Medical Image Segmentation
Rousseau, A.-J., Becker, T., Bertels, J., Blaschko, M. B., & Valkenborg, D. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434131 · Cited by 25 · see citing works
Mixture of calibrated networks for domain generalization in brain tumor segmentation
Hu, J., Gu, X., Wang, Z., & Gu, X. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110520 · Cited by 25 · see citing works
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
Park, H.-K., Noh, J., Oh, Y., Baek, D., & Ham, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00364 · Cited by 25 · see citing works
MECardNet: A novel multi-scale convolutional ensemble model with adaptive deep supervision for precise cardiac MRI segmentation
Aghapanah, H., Rasti, R., Tabesh, F., Pouraliakbar, H., Sanei, H., & Kermani, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106919 · Cited by 26 · see citing works
A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans
Yu, Z., Han, X., Xu, W., Zhang, J., Marr, C., Shen, D. G., Peng, T., Zhang, X., & Feng, J. (2022) · eLife · doi:10.7554/elife.81217 · Cited by 24 · see citing works
Hybrid dual mean-teacher network with double-uncertainty guidance for semi-supervised segmentation of magnetic resonance images
Zhu, J., Bolsterlee, B., Chow, B. V. Y., Song, Y., & Meijering, E. H. W. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102383 · Cited by 28 · see citing works
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
Oberdiek, P., Rottmann, M., & Fink, G. A. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00172 · Cited by 23 · see citing works
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning
Sahlsten, J., Jaskari, J., Wahid, K. A., Ahmed, S., Glerean, E., He, R., Kann, B. H., Mäkitie, A. A., Fuller, C. D., Naser, M. A., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00528-5 · Cited by 21 · see citing works
Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond
Meyer, A., Ghosh, S., Schindele, D., Schostak, M., Stober, S., Hansen, C., & Rak, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102073 · Cited by 21 · see citing works
Orthogonal Ensemble Networks for Biomedical Image Segmentation
Larrazabal, A. J., Mart'inez, C., Dolz, J., & Ferrante, E. (2021) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-87199-4_56 · Cited by 26 · see citing works
Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation
Zenk, M., Zimmerer, D., Isensee, F., Traub, J., Norajitra, T., Jäger, P. F., & Maier-Hein, K. H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103392 · Cited by 22 · see citing works
Uncertainty-Aware Medical Diagnostic Phrase Identification and Grounding
Zou, K., Bai, Y., Liu, B., Chen, Y., Chen, Z., Zhou, Y., Yuan, X., Wang, M., Shen, X., Cao, X., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3596878 · Cited by 29 · see citing works
Medical image segmentation using scalable functional variational Bayesian neural networks with Gaussian processes
Xu, C. G., Zhao, Y., & Liu, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.055 · Cited by 20 · see citing works
Uncertainty Estimation for Heatmap-Based Landmark Localization
Schobs, L., Swift, A. J., & Lu, H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3222730 · Cited by 20 · see citing works
TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation
Uslu, F., & Bharath, A. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106422 · Cited by 20 · see citing works
Semi-supervised segmentation of coronary DSA using mixed networks and multi-strategies
Pu, Y., Zhang, Q., Qian, C., Zeng, Q., Li, N., Zhang, L., Zhou, S., & Zhao, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106493 · Cited by 20 · see citing works
Uncertainty-guided transformer for brain tumor segmentation
Chen, Z., Peng, C., Guo, W., Xie, L., Wang, S., Zhuge, Q., Wen, C., & Feng, Y. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02899-8 · Cited by 20 · see citing works
Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer
Arega, T. W., Bricq, S., Legrand, F., Jacquier, A., Lalande, A., & Mériaudeau, F. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102773 · Cited by 19 · see citing works
Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation
Zhu, Z., Ma, X., Wang, W., Dong, S., Wang, K., Wu, L., Luo, G., Wang, G., & Li, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103112 · Cited by 19 · see citing works
VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering
Ye, H., Zhang, X., Hu, Y., Fu, H., & Liu, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526061 · Cited by 19 · see citing works
Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics
Zhao, J., Lin, S.-Y., Attias, R., Mathews, L., Engel, C., Larghero, G., Vremenko, D., Kao, T., Lee, T.-H., Wang, Y.-H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64249-6 · Cited by 19 · see citing works
Domain adaptation for segmentation of critical structures for prostate cancer therapy
Meyer, A., Mehrtash, A., Rak, M., Bashkanov, O., Langbein, B. J., Ziaei, A., Kibel, A. S., Tempany, C. M., Hansen, C., & Tokuda, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-90294-4 · Cited by 18 · see citing works
Self-citation
Graph Node Based Interpretability Guided Sample Selection for Active Learning
Mahapatra, D., Poellinger, A., & Reyes, M. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3215017 · Cited by 18 · see citing works
Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout
Klaneček, Ž., Wagner, T., Wang, Y., Cockmartin, L., Marshall, N., Schott, B., Deatsch, A. E., Studen, A., Hertl, K., Jarm, K., et al. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acd221 · Cited by 18 · see citing works
Latent Transformer Models for out-of-distribution detection
Graham, M., Tudosiu, P.-D., Wright, P., Pinaya, W. H. L., Teikari, P., Patel, A., U-King-im, J., Mah, Y., Teo, J., Jäger, H., et al. (2023) · Medical Image Anal. · doi:10.1016/j.media.2023.102967 · Cited by 16 · see citing works
Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis
Dong, T., Sinha, S., Zhai, B., Fudulu, D. P., Chan, J., Narayan, P., Judge, A., Caputo, M., Dimagli, A., Benedetto, U., et al. (2024) · JMIRx Med · doi:10.2196/45973 · Cited by 18 · see citing works
EvidenceCap: Towards trustworthy medical image segmentation via evidential identity capPreprint
Zou, K., Yuan, X., Shen, X., Chen, Y., Wang, M., Goh, R. S. M., Liu, Y., & Fu, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2558155/v1 · Cited by 18 · see citing works
Confidence Calibration for Object Detection and Segmentation
Küppers, F., Haselhoff, A., Kronenberger, J., & Schneider, J. (2022) · Springer eBooks · doi:10.1007/978-3-031-01233-4_8 · Cited by 19 · see citing works
Model Ensemble With Dropout for Uncertainty Estimation in Sea Ice Segmentation Using Sentinel-1 SAR
de Lima, R. P., & Karimzadeh, M. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3331276 · Cited by 17 · see citing works
Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images
Ren, J., Teuwen, J., Nijkamp, J. A., Rasmussen, M. E., Gouw, Z. A. R., Eriksen, J. G., Sonke, J., & Korreman, S. S. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad682d · Cited by 18 · see citing works
The Centerline-Cross Entropy Loss for Vessel-Like Structure Segmentation: Better Topology Consistency Without Sacrificing Accuracy
Acebes, C., Moustafa, A.-H., Cámara, Ó., & Galdrán, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_67 · Cited by 17 · see citing works
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
Soufi, M., Otake, Y., Iwasa, M., Uemura, K., Hakotani, T., Hashimoto, M., Yamada, Y., Yamada, M., Yokoyama, Y., Jinzaki, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-024-83793-7 · Cited by 16 · see citing works

Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network
Gasmi, K., Ammar, L. B., Elshammari, H., & Yahya, F. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033270 · Cited by 2 · see citing works
Trustseg-Net: Uncertainty-Calibrated Medical Image Segmentation with Boundary-Aware Confidence Estimation
Murugan, M. N. S., Prathanya, P. A., Abinaya, M., Abisha, D., Epziba, V., & Akilesh, M. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22206313
Toward Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
Zou, K., Chen, Y., Huang, L., Zhou, N., Yuan, X., Shen, X., Wang, M., Goh, R. S. M., Liu, Y., Tham, Y. C., et al. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3604432 · Cited by 24 · see citing works
Comparative analysis of loss functions for uncertainty quantification in medical image segmentation
Kethavath, S., Vasundhara, & Yalavarthy, P. K. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae2b73 · Cited by 1 · see citing works
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated UncertaintyPreprint
Zou, K., Chen, Y., Huang, L., Yuan, X., Shen, X., Wang, M., Goh, R. S. M., Liu, Y., & Fu, H. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.00349 · Cited by 1 · see citing works

Authors: Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P., & Kapur, T.
Affiliations: Brigham and Women's Hospital (US)
University of British Columbia (CA)
ORCID: Mehrtash, Alireza
Wells, William M.
Abolmaesumi, Purang
Kapur, Tina
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-12
Volume: 39
Issue: 12
Pages: 3868-3878
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2020.3006437
PMC: PMC7704933
OpenAlex: W2991523350
Cited by: 428
References: 55
Citations by year: 2026: 91 · 2025: 92 · 2024: 71 · 2023: 71 · 2022: 56 · 2021: 41 · 2020: 6
FWCI: 16.91
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: U.S. National Institutes of Health (P41EB015898)
Natural Sciences and Engineering Research Council (NSERC) of Canada
Canadian Institutes of Health Research
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: confidence calibration, predictive uncertainty estimation, medical image segmentation, fully convolutional network, U-Net, Dice loss, cross-entropy loss, model ensembling, out-of-distribution detection, batch normalization, brain segmentation, cardiac image segmentation, prostate segmentation
MeSH terms: Brain, Calibration, Humans, Image Processing, Computer-Assisted, Male, Neural Networks, Computer, Uncertainty

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Confidence calibration and predictive uncertainty estimation for deep medical image segmentation

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