Pith. sign in

REVIEW 2 major objections 1 minor 38 references

Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation

T0 review · 2 major / 1 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Segmentation quality can be predicted without ground-truth labels via Bayesian uncertainty, with $R^2$ = 93.25 on skin lesions and 85.03 on liver.

desk verdict Interesting framework undermined by an internally inconsistent liver metric. read the letter →

arxiv 2508.01460 v1 pith:E45CVB3U submitted 2025-08-02 cs.CV

Sikha O K , Meritxell Riera-Marín , Adrian Galdran , Javier García Lopez , Julia Rodríguez-Comas , Gemma Piella , Miguel A. González Ballester This is my paper · ORCID
classification cs.CV
keywords segmentationqualityassessmentuncertaintyestimationBayesiandeeplearningMonteCarloDropouttest-timeaugmentationensembleskinlesionliver
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that segmentation quality can be predicted reliably without ground-truth annotations at test time, by training a regressor on predicted segmentation maps and Bayesian uncertainty estimates. If the framework holds, clinical tools could flag low-quality segmentations and decide when human review is needed. The authors demonstrate it on 2D skin lesion and 3D liver segmentation, attaining an $R^2$ of 93.25 and Pearson correlation of 96.58 on HAM10000, and $R^2$ of 85.03 with test-time augmentation plus entropy on liver, arguing that the method transfers across modalities. The paper also proposes fusing multiple uncertainty estimates into one per-image score and uses visual attribution to interpret the learned quality signal.

What carries the argument

The load-bearing machinery is the uncertainty-aware quality regressor: a network that consumes either (predicted segmentation, uncertainty map) or (input image, uncertainty map, predicted segmentation) and outputs a predicted Dice score. Uncertainty maps are generated by Bayesian adaptations of SwinUNet and FPN-ResNet50 using Monte Carlo Dropout, ensembles, or test-time augmentation, then summarized by confidence, entropy, mutual information, or expected pairwise KL divergence. A proposed aggregation strategy combines multiple uncertainty estimates into a single per-image score. Grad-CAM and UMAP embeddings are used to show which regions of the input drive the quality prediction.

What would settle it

Measure the correlation between the framework's predicted quality and the true Dice coefficient on a new organ outside the training distribution, such as brain MRI, without fine-tuning; if the $R^2$ falls below the level needed to separate good from bad segmentations, the generalization claim fails. A sharper test is to feed the network a segmentation whose errors are deliberately designed not to change the uncertainty estimate, such as a uniform inward shift of all boundaries; if the predicted quality stays high while Dice drops sharply, uncertainty is not actually carrying the error signal.

Watch

Extended reading notes

Core claim

The paper's central claim is that segmentation quality can be scored without ground truth by learning a mapping from predicted segmentation and uncertainty maps to the Dice coefficient. Two complementary network inputs are compared: predicted segmentation plus uncertainty, and original image plus uncertainty plus predicted segmentation. Bayesian adaptations of SwinUNet and FPN-ResNet50 provide three uncertainty sources (Monte Carlo Dropout, ensembles, and test-time augmentation) and four uncertainty metrics (confidence, entropy, mutual information, and expected pairwise KL divergence). On HAM10000 skin lesions the best configuration reaches $R^2 = 93.25$ and Pearson $r = 96.58$, outperforming prior segmentation quality assessment methods; on 3D liver data, test-time augmentation with entropy yields $R^2 = 85.03$ and Pearson $r = 65.02$. These numbers are the paper's evidence that the learned relationship between uncertainty and error generalizes across modalities.

Load-bearing premise

The framework rests on the assumption that uncertainty estimates from Monte Carlo Dropout, ensembles, or test-time augmentation are reliable proxies for actual segmentation error, and that the quality regressor trained on one set of images keeps working on unseen organs and protocols without re-training.

Editorial extensions

If this is right

  • If accurate, these methods let segmentation models report a confidence grade on every output, so clinicians know when to trust an automatic contour and when to edit or re-run.
  • The same regressor could be applied to any segmentation network, not only the two backbones tested, since it consumes generic segmentation and uncertainty maps.
  • The aggregation strategy produces a single score from complementary uncertainty signals, simplifying deployment in a clinical workflow.
  • The cross-modality result (2D skin, 3D liver) indicates the learned error signal transfers rather than overfitting to one dataset, opening the door to broader medical use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The gap between Pearson correlation (65.02) and $R^2$ (85.03) on liver data suggests the predicted scores track the overall trend but are less accurate per-case outside the training distribution; a stricter test would examine calibration on a third organ.
  • If uncertainty is a valid proxy for error, the same framework could drive active learning, telling a lab which unlabeled images to annotate next because their segmentations are most likely wrong.
  • Decoupling the two sources of error could be done by feeding an oracle uncertainty (computed from the true error map) into the regressor and comparing against the model's uncertainty; this would isolate the quality regressor's ceiling.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper proposes a framework for predicting segmentation quality (e.g., Dice) at test time without ground truth, using Bayesian uncertainty maps derived from Monte Carlo Dropout, ensembles, and Test Time Augmentation applied to SwinUNet and Feature Pyramid Network with ResNet50. The abstract reports very high predictive performance on the HAM10000 skin-lesion dataset (R² = 93.25, Pearson r = 96.58) and claims cross-modality robustness from a 3D liver segmentation experiment (R² = 85.03, Pearson r = 65.02). The paper also describes an aggregation strategy for combining multiple uncertainty estimates and uses Grad-CAM and UMAP for interpretability.

Significance. If the reported results are correct and reproducible, the framework would provide a practically useful way to flag unreliable segmentations in clinical workflows without manual annotations. The general idea—learning a supervised regressor from uncertainty maps to segmentation quality—is methodologically sound and nicely complements existing segmentation-quality-assessment literature. The inclusion of interpretability analysis is a strength, as it may help clinicians trust the uncertainty predictions. However, the numerical inconsistency in the abstract's liver metrics currently prevents crediting the cross-modality claim, and the abstract does not provide enough protocol detail to rule out overfitting or leakage in the near-perfect HAM10000 scores.

major comments (2)
  1. [Abstract (liver segmentation results)] The reported pair R² = 85.03 and Pearson correlation = 65.02 cannot both be correct under standard definitions for the same model and evaluation set. For any model predictions and ground-truth values, R² is bounded above by r², because the least-squares linear fit of ground truth on predictions always achieves at least as high R² as the model's own prediction mapping. Here r² = 0.6502² ≈ 0.4228, far below the stated R² = 0.8503. The manuscript must clarify whether the two metrics were computed on different subsets, different targets, or with different averaging (e.g., per-image versus per-volume Dice), or correct the values. Without this clarification, the claim of cross-modality robustness is unsupported.
  2. [Abstract (HAM10000 results)] The abstract reports R² = 93.25 and Pearson r = 96.58 on HAM10000 but gives no evaluation protocol: no training/test split, no cross-validation scheme, no number of segmentation models or uncertainty estimates averaged, and no error bars. Since the uncertainty maps are generated by the same segmentation models whose quality is predicted, and since near-perfect scores can result from overfitting or information leakage (for example, inadvertently using test-set quality during training), the full manuscript must provide a complete experimental protocol and statistical analysis to support the claim of outperforming previous segmentation quality assessment methods.
minor comments (1)
  1. [Abstract] The abbreviation 'TTA' is used for Test Time Augmentation; please spell it out at first use, and consistently use the same notation for the coefficient of determination (R²) and the Pearson correlation coefficient (r).

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning identified: the framework is a supervised quality-prediction regression trained on uncertainty estimates, not a derivation that presupposes its target.

full rationale

The paper proposes a supervised framework that predicts segmentation quality (e.g., Dice) from uncertainty maps and, in one variant, the input image plus predicted segmentation. The uncertainty maps are generated by Bayesian adaptations of segmentation models using Monte Carlo Dropout, Ensemble, and Test Time Augmentation. This is a legitimate predictive setup: the model learns a statistical relationship between uncertainty features and quality metrics, and at test time no ground-truth annotations are required. There is no evidence that the uncertainty estimates are defined in terms of the Dice score, nor that the quality prediction is fitted to the same quantity it claims to predict. The reported improvement over prior methods is an empirical claim, not a circular one. The noted inconsistency between R2 and Pearson correlation for the liver experiment (R2=85.03 vs Pearson=65.02, with R2 exceeding r^2) is a serious internal-consistency/correctness concern, but it is not a circularity: it does not show that the result reduces to its inputs by construction. The abstract contains no self-citation, no imported uniqueness theorem, and no ansatz smuggled via citation. Therefore, under the stated rules, no significant circularity is found.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

No free parameters are reported in the abstract. The key assumption is the correlation between uncertainty and segmentation quality, which the framework trains on. No new entities beyond the trained models are introduced.

assumptions (1)
  • domain assumption Uncertainty estimates correlate with segmentation quality
    The framework assumes that Monte Carlo Dropout, ensemble, and test-time augmentation uncertainty are predictive of Dice score, an assumption stated but not proven in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation." pith.science (2026). https://pith.science/paper/E45CVB3U

@misc{pith2026250801460,
  author       = {Pith},
  title        = {Pith review of: Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E45CVB3U}},
  note         = {Machine review of arXiv:2508.01460}
}
read the original abstract

Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual annotations, assessing segmentation quality becomes challenging, and models lacking reliability indicators face adoption barriers. To address this gap, we propose a novel framework for predicting segmentation quality without requiring ground truth annotations during test time. Our approach introduces two complementary frameworks: one leveraging predicted segmentation and uncertainty maps, and another integrating the original input image, uncertainty maps, and predicted segmentation maps. We present Bayesian adaptations of two benchmark segmentation models-SwinUNet and Feature Pyramid Network with ResNet50-using Monte Carlo Dropout, Ensemble, and Test Time Augmentation to quantify uncertainty. We evaluate four uncertainty estimates: confidence map, entropy, mutual information, and expected pairwise Kullback-Leibler divergence on 2D skin lesion and 3D liver segmentation datasets, analyzing their correlation with segmentation quality metrics. Our framework achieves an R2 score of 93.25 and Pearson correlation of 96.58 on the HAM10000 dataset, outperforming previous segmentation quality assessment methods. For 3D liver segmentation, Test Time Augmentation with entropy achieves an R2 score of 85.03 and a Pearson correlation of 65.02, demonstrating cross-modality robustness. Additionally, we propose an aggregation strategy that combines multiple uncertainty estimates into a single score per image, offering a more robust and comprehensive assessment of segmentation quality. Finally, we use Grad-CAM and UMAP-based embedding analysis to interpret the model's behavior and reliability, highlighting the impact of uncertainty integration.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 32 canonical work pages

  1. [1]

    R. Nock, F. Nielsen, Statistical region merging, IEEE Transactions on pattern analysis and machine intelligence 26 (11) (2004) 1452--1458

    work page 2004

  2. [2]

    T. Lei, P. Liu, X. Jia, X. Zhang, H. Meng, A. K. Nandi, Automatic fuzzy clustering framework for image segmentation, IEEE Transactions on Fuzzy Systems 28 (9) (2019) 2078--2092

    work page 2019

  3. [3]

    Valindria et al., Reverse classification accuracy: predicting segmentation performance in the absence of ground truth, IEEE transactions on medical imaging 36 (8) (2017) 1597--1606

    V. Valindria et al., Reverse classification accuracy: predicting segmentation performance in the absence of ground truth, IEEE transactions on medical imaging 36 (8) (2017) 1597--1606

    work page 2017

  4. [4]

    Y. Wang, Y. Zhou, W. Shen, S. Park, E. K. Fishman, A. L. Yuille, Abdominal multi-organ segmentation with organ-attention networks and statistical fusion, Medical image analysis 55 (2019) 88--102

    work page 2019

  5. [5]

    Somasundaram, R

    S. Somasundaram, R. Gobinath, Current trends on deep learning models for brain tumor segmentation and detection--a review, in: 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), IEEE, 2019, pp. 217--221

    work page 2019

  6. [6]

    Q. Lin, X. Chen, C. Chen, J. M. Garibaldi, A novel quality control algorithm for medical image segmentation based on fuzzy uncertainty, IEEE Transactions on Fuzzy Systems (2022)

    work page 2022

  7. [7]

    F. Ge, S. Wang, T. Liu, New benchmark for image segmentation evaluation, Journal of Electronic Imaging 16 (3) (2007) 033011--033011

    work page 2007

  8. [8]

    Movahedi, J

    V. Movahedi, J. H. Elder, Design and perceptual validation of performance measures for salient object segmentation, in: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, IEEE, 2010, pp. 49--56

    work page 2010

Show all 38 references
  1. [9]

    D. P. Huttenlocher, G. A. Klanderman, W. J. Rucklidge, Comparing images using the hausdorff distance, IEEE Transactions on pattern analysis and machine intelligence 15 (9) (1993) 850--863

    1993

  2. [10]

    Robinson et al., Subject-level prediction of segmentation failure using real-time convolutional neural nets (2018)

    R. Robinson et al., Subject-level prediction of segmentation failure using real-time convolutional neural nets (2018)

    2018

  3. [11]

    M. Ng, F. Guo, L. Biswas, S. E. Petersen, S. K. Piechnik, S. Neubauer, G. Wright, Estimating uncertainty in neural networks for cardiac mri segmentation: A benchmark study, IEEE Transactions on Biomedical Engineering (2022)

    2022

  4. [12]

    L. Zhou, W. Deng, X. Wu, Robust image segmentation quality assessment, arXiv preprint arXiv:1903.08773 (2019)

    2019 arXiv

  5. [13]

    Abdar, F

    M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya, et al., A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Information fusion 76 (2021) 243--297

    2021

  6. [14]

    DeVries, G

    T. DeVries, G. W. Taylor, Leveraging uncertainty estimates for predicting segmentation quality, arXiv preprint arXiv:1807.00502 (2018)

    2018 arXiv

  7. [15]

    Sikha, A

    O. Sikha, A. Galdran, M. Riera-Marin, J. Garc \' a, J. Rodr \' guez-Comas, G. Piella, M. A. G. Ballester, Uncertainty aware segmentation quality assessment in medical images, in: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, 2024, pp. 1--5

    2024

  8. [16]

    Konyushkova, R

    K. Konyushkova, R. Sznitman, P. Fua, Learning active learning from data, Advances in neural information processing systems 30 (2017)

    2017

  9. [17]

    Kohlberger, V

    T. Kohlberger, V. Singh, C. Alvino, C. Bahlmann, L. Grady, Evaluating segmentation error without ground truth, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2012, pp. 528--536

    2012

  10. [18]

    A. G. Roy, S. Conjeti, N. Navab, C. Wachinger, A. D. N. Initiative, et al., Bayesian quicknat: Model uncertainty in deep whole-brain segmentation for structure-wise quality control, NeuroImage 195 (2019) 11--22

    2019

  11. [19]

    H. Chen, R. F. Murphy, Evaluation of cell segmentation methods without reference segmentations, Molecular Biology of the Cell 34 (6) (2023) ar50

    2023

  12. [20]

    R. M. Neal, Bayesian learning for neural networks, Vol. 118, Springer Science & Business Media, 2012

    2012

  13. [21]

    Y. Gal, Z. Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning, in: international conference on machine learning, PMLR, 2016, pp. 1050--1059

    2016

  14. [22]

    Louizos, M

    C. Louizos, M. Welling, Multiplicative normalizing flows for variational bayesian neural networks, in: International Conference on Machine Learning, PMLR, 2017, pp. 2218--2227

    2017

  15. [23]

    Atanov, A

    A. Atanov, A. Ashukha, D. Molchanov, K. Neklyudov, D. Vetrov, Uncertainty estimation via stochastic batch normalization, in: Advances in Neural Networks--ISNN 2019: 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10--12, 2019, Proceedings, Part...

    2019

  16. [24]

    G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, T. Vercauteren, Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks, Neurocomputing 338 (2019) 34--45

    2019

  17. [25]

    Lakshminarayanan, A

    B. Lakshminarayanan, A. Pritzel, C. Blundell, Simple and scalable predictive uncertainty estimation using deep ensembles, Advances in neural information processing systems 30 (2017)

    2017

  18. [26]

    K.-C. Kahl, C. T. L \"u th, M. Zenk, K. Maier-Hein, P. F. Jaeger, Values: A framework for systematic validation of uncertainty estimation in semantic segmentation, arXiv preprint arXiv:2401.08501 (2024)

    2024 arXiv

  19. [27]

    H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, M. Wang, Swin-unet: Unet-like pure transformer for medical image segmentation, in: European conference on computer vision, Springer, 2022, pp. 205--218

    2022

  20. [28]

    T.-Y. Lin, P. Doll \'a r, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature pyramid networks for object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117--2125

    2017

  21. [29]

    Y. Tang, D. Yang, W. Li, H. R. Roth, B. Landman, D. Xu, V. Nath, A. Hatamizadeh, Self-supervised pre-training of swin transformers for 3d medical image analysis, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 20730--20740

    2022

  22. [30]

    Esteva, B

    A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, nature 542 (7639) (2017) 115--118

    2017

  23. [31]

    Codella, V

    N. Codella, V. Rotemberg, P. Tschandl, M. E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchetti, et al., Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic), arXiv preprint arXi...

    2019 arXiv

  24. [32]

    J. Liu, J. Paisley, M.-A. Kioumourtzoglou, B. Coull, Accurate uncertainty estimation and decomposition in ensemble learning, Advances in neural information processing systems 32 (2019)

    2019

  25. [33]

    Molchanova, V

    N. Molchanova, V. Raina, A. Malinin, F. La Rosa, H. Muller, M. Gales, C. Granziera, M. Graziani, M. B. Cuadra, Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis, in: 2023 IEEE 20th International Symposium on Biomedical Im...

    2023

  26. [34]

    Tschandl, C

    P. Tschandl, C. Rosendahl, H. Kittler, The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Scientific data 5 (1) (2018) 1--9

    2018

  27. [35]

    Bilic, P

    P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, S. Kadoury, T. Konopczynski, M. Le, C. Li, X. Li, J. Lipkovà, J. Lowengrub, H. Meine, J. H. Moltz, C. Pal, M. Piraud, X. Qi, J. Qi, M. Rempfler, K. Roth, A. Schenk, A. S...

    2019 arXiv

  28. [36]

    DeVries, G

    T. DeVries, G. W. Taylor, Learning confidence for out-of-distribution detection in neural networks, arXiv preprint arXiv:1802.04865 (2018)

    2018 arXiv

  29. [37]

    Huang, Q

    C. Huang, Q. Wu, F. Meng, Qualitynet: Segmentation quality evaluation with deep convolutional networks, in: 2016 Visual Communications and Image Processing (VCIP), IEEE, 2016, pp. 1--4

    2016

  30. [38]

    Kendall, Y

    A. Kendall, Y. Gal, What uncertainties do we need in bayesian deep learning for computer vision?, Advances in neural information processing systems 30 (2017)

    2017

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.