Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation

Hoseok Lee; Jaeyoon Sim; Jihwan Park; Seunghun Baek; Seungjoo Lee; Won Hwa Kim

REVIEW 3 major objections 2 minor 21 references

Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation

T0 review · 3 major / 2 minor · reviewed 2026-06-30 · grok-4.3

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

Pith's one-line read Modeling representations as Gaussians with mean regularization and set-inclusive variance scaling captures uncertainty from missing MRI modalities in brain tumor segmentation.

desk verdict The paper introduces Gaussian feature modeling with mean regularization to full modalities, discrepancy-based variance scaling, and set-inclusive ordering for missing-modality brain tumor segmentation, showing gains on BraTS but only indirect checks on whether variance tracks information loss. read the letter →

arxiv 2606.30374 v1 pith:H6EBFOCV submitted 2026-06-29 cs.CV cs.AIcs.LG

Seunghun Baek , Jihwan Park , Jaeyoon Sim , Hoseok Lee , Seungjoo Lee , Won Hwa Kim This is my paper · ORCID

classification cs.CVcs.AIcs.LG

keywords braintumorsegmentationmissingmodalitiesuncertaintymodelingmultimodalMRIGaussiandistributionsset-inclusivestrategyBraTSdataset

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

The paper establishes a probabilistic framework that represents feature encodings as Gaussian distributions for multimodal brain tumor segmentation. The mean encodes the segmentation task information while the variance is intended to quantify uncertainty arising from missing modalities. It achieves this by pulling each partial-modality mean toward the corresponding full-modality mean and scaling the variance by the size of that discrepancy, then imposes a set-inclusive ordering constraint across modality subsets to keep uncertainty relations consistent. A sympathetic reader would care because real clinical scans frequently lack one or more MRI sequences, causing deterministic models to output plausible but unreliable segmentations. Experiments on BraTS 2018 and 2020 show the method outperforms baselines under many different missing-modality patterns.

What carries the argument

Gaussian distributions for representations whose means are regularized from partial to full modality configurations and whose variances are scaled by mean discrepancy, subject to a set-inclusive ordering constraint on modality subsets.

What would settle it

On BraTS 2018 or 2020 test cases with held-out missing-modality combinations, if the method shows no gain in Dice or Hausdorff scores over strong deterministic baselines and its predicted variances fail to correlate with actual segmentation errors, the central modeling claim would be falsified.

Watch

Extended reading notes

Core claim

We propose a probabilistic representation framework that models representations as Gaussian distributions, where their mean captures task information and their variance measures uncertainty from missing evidence. To make variance reflect information deficiency, we regularize the mean from each partial configuration toward its full-modality counterpart, while scaling the variance with the discrepancy between their aligned means. We further introduce a set-inclusive strategy that exploits the hierarchical structure of modality subsets and enforces an ordering constraint to maintain their consistent uncertainty relationships.

Load-bearing premise

That regularizing each partial-modality mean toward its full-modality counterpart and scaling variance by the resulting discrepancy, together with the set-inclusive ordering constraint, will make the variance accurately reflect the information lost from missing modalities.

Editorial extensions

If this is right

The model produces superior segmentation accuracy over baselines across diverse missing-modality scenarios on BraTS 2018 and 2020.
Variance values reflect the degree of information deficiency caused by absent modalities.
Uncertainty relationships remain consistent across different subsets of available modalities due to the ordering constraint.
The framework avoids encoding incomplete evidence into overconfident deterministic representations.

Reading between the lines

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

The same mean-regularization and variance-scaling approach could be tested on other multimodal medical imaging tasks such as cardiac or abdominal segmentation.
Uncertainty maps produced by the model might be used in clinical workflows to flag cases where additional scans would most reduce segmentation error.
A direct check of whether higher predicted variance on a given voxel predicts higher error rates when that modality is missing would provide an independent validation signal.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The core idea is to represent features as Gaussians where the mean holds the segmentation signal and variance is meant to capture uncertainty from missing MRI modalities. They regularize each partial-modality mean toward the full-modality mean, scale variance by the size of that discrepancy, and add a set-inclusive ordering constraint so uncertainty stays consistent across different modality subsets. Experiments on BraTS 2018 and 2020 report better segmentation numbers than the baselines they compare against, and they release code and checkpoints.

What stands out is the explicit handling of the subset structure of modalities through the ordering constraint, plus the decision to tie variance directly to the mean gap rather than learning it freely. Releasing the implementation is useful for anyone who wants to test the pieces.

The soft spot is the missing link between the variance construction and actual information deficiency. The paper validates the whole system through downstream segmentation metrics, but does not show separate evidence that the scaled variance correlates with missing data volume or that the ordering constraint is satisfied in the trained networks. If the mean discrepancy is driven by training artifacts or modality biases instead of missing evidence, the uncertainty modeling does not deliver the reliability benefit claimed.

This is aimed at groups working on robust multimodal medical segmentation. The method is concrete enough and the results are clear enough that it should go to referees rather than a desk reject, though reviewers will likely ask for direct validation of the uncertainty quantities.

Referee Report

3 major / 2 minor

Summary. The paper proposes a probabilistic representation framework for robust brain tumor segmentation under missing MRI modalities. Representations are modeled as Gaussians where the mean encodes task-relevant information and the variance is intended to quantify uncertainty due to missing evidence. This is achieved by regularizing each partial-modality mean toward its full-modality counterpart and scaling the variance proportionally to the discrepancy between these aligned means; a set-inclusive strategy with an ordering constraint is introduced to enforce consistent uncertainty relationships across modality subsets. Extensive experiments on BraTS 2018 and 2020 are reported to demonstrate superior performance over baselines across diverse missing-modality scenarios.

Significance. If the core modeling assumptions hold, the approach could provide more reliable uncertainty estimates in clinical scenarios where not all modalities are available, addressing a practical limitation in multimodal medical imaging. The public release of code and checkpoints strengthens reproducibility.

major comments (3)

[§3] §3 (Method), the variance scaling construction: the claim that scaling variance with the mean discrepancy causes variance to 'reflect information deficiency' is a central modeling assumption, yet the experiments report only segmentation metrics (Dice, etc.) with no direct validation such as correlation between learned variance and ground-truth information loss or ablation on whether discrepancy is dominated by missing evidence versus optimization artifacts.
[§3.2] §3.2 (Set-inclusive strategy): the ordering constraint is presented as enforcing consistent uncertainty relationships across modality subsets, but no quantitative verification is provided that this constraint is satisfied in the trained models (e.g., empirical checks on variance ordering for nested modality sets).
[§4] §4 (Experiments): while superiority on BraTS 2018/2020 is claimed across missing-modality scenarios, the evaluation lacks controls that isolate whether performance gains stem from the uncertainty modeling versus other implementation choices (e.g., the regularization strength or network architecture differences from baselines).

minor comments (2)

[§3] Notation for the Gaussian parameters (mean and variance) should be introduced with explicit equations early in §3 to avoid ambiguity when referring to 'aligned means'.
[§4] Figure captions for qualitative results should explicitly state which modality subsets are shown and whether the displayed uncertainty maps correspond to the proposed variance term.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below and indicate planned revisions to the manuscript.

read point-by-point responses

Referee: [§3] §3 (Method), the variance scaling construction: the claim that scaling variance with the mean discrepancy causes variance to 'reflect information deficiency' is a central modeling assumption, yet the experiments report only segmentation metrics (Dice, etc.) with no direct validation such as correlation between learned variance and ground-truth information loss or ablation on whether discrepancy is dominated by missing evidence versus optimization artifacts.

Authors: We acknowledge that direct validation of the central modeling assumption would strengthen the presentation. While the consistent gains in segmentation metrics under missing-modality conditions provide supporting evidence for the design, we agree that additional analysis is needed. In the revised manuscript we will add an ablation correlating learned variance with mean discrepancy and an analysis separating the contribution of missing evidence from optimization effects, to be placed in Section 4. revision: yes
Referee: [§3.2] §3.2 (Set-inclusive strategy): the ordering constraint is presented as enforcing consistent uncertainty relationships across modality subsets, but no quantitative verification is provided that this constraint is satisfied in the trained models (e.g., empirical checks on variance ordering for nested modality sets).

Authors: The ordering constraint is imposed via the hierarchical loss during training. We recognize the value of post-hoc empirical verification. We will include quantitative checks confirming that variance ordering holds for nested modality subsets in the trained models; these results will be added to the experiments section of the revised paper. revision: yes
Referee: [§4] §4 (Experiments): while superiority on BraTS 2018/2020 is claimed across missing-modality scenarios, the evaluation lacks controls that isolate whether performance gains stem from the uncertainty modeling versus other implementation choices (e.g., the regularization strength or network architecture differences from baselines).

Authors: The reported baselines follow the original implementations in the literature, and our backbone is chosen for comparability. To more clearly isolate the contribution of the uncertainty modeling and set-inclusive components, we will add ablation studies on regularization strength together with architecture-matched comparisons in the revised experiments section. revision: yes

Circularity Check

1 steps flagged · score 6.0 of 10

Variance scaling by mean discrepancy enforces reflection of information deficiency by construction

self definitional [Abstract]
"To make variance reflect information deficiency, we regularize the mean from each partial configuration toward its full-modality counterpart, while scaling the variance with the discrepancy between their aligned means."

The paper states its goal (variance reflects information deficiency) and immediately defines the mechanism (scale variance by mean discrepancy) to achieve that goal. The asserted property of the variance is therefore true by the construction of the representation rather than derived from first principles or validated against an independent measure of information loss.

full rationale

The paper's core claim is that variance measures uncertainty from missing evidence. This is achieved by an explicit design rule that scales variance with the discrepancy between partial-configuration and full-modality means after regularization. The relationship therefore holds by the model's definitional construction rather than by independent derivation or external validation. The set-inclusive ordering constraint adds structure but inherits the same definitional premise. No self-citations, fitted parameters renamed as predictions, or uniqueness theorems appear in the provided text, so circularity is partial and localized to the uncertainty modeling step.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on domain assumptions about probabilistic encoding of uncertainty and ad-hoc modeling choices for regularization and ordering that are introduced to address missing modalities; no free parameters or invented entities are explicitly quantified in the abstract.

assumptions (3)

domain assumption Representations can be modeled as Gaussian distributions where the mean captures task information and the variance measures uncertainty from missing evidence.
Core of the probabilistic representation framework stated in the abstract.
ad hoc to paper Regularizing partial-configuration means toward full-modality counterparts and scaling variance with mean discrepancy will make variance reflect information deficiency.
Key proposed mechanism for tying variance to missing evidence.
ad hoc to paper The hierarchical structure of modality subsets can be exploited with an ordering constraint to maintain consistent uncertainty relationships.
Basis for the set-inclusive strategy.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation." pith.science (2026). https://pith.science/paper/H6EBFOCV

@misc{pith2026260630374,
  author       = {Pith},
  title        = {Pith review of: Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H6EBFOCV}},
  note         = {Machine review of arXiv:2606.30374}
}

read the original abstract

Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertainty due to unavoidable information loss. Without modeling this uncertainty, existing methods encode incomplete evidence into deterministic representations that appear plausible but lack reliability. In this regime, we propose a probabilistic representation framework that models representations as Gaussian distributions, where their mean captures task information and their variance measures uncertainty from missing evidence. To make variance reflect information deficiency, we regularize the mean from each partial configuration toward its full-modality counterpart, while scaling the variance with the discrepancy between their aligned means. We further introduce a set-inclusive strategy that exploits the hierarchical structure of modality subsets and enforces an ordering constraint to maintain their consistent uncertainty relationships. Extensive experiments on BraTS 2018 and 2020 demonstrate that our approach offers superior performance over baselines across diverse missing-modality scenarios. Code and model checkpoint are available at https://github.com/atlas-sky/SIUM.

Figures

Figures reproduced from arXiv: 2606.30374 by the authors.

**Figure 1.** Overall framework. (a) For each sample xi, three modality sets S sub i ⊂ Ssup i ⊂ S full i are constructed and then processed independently. Subset configurations are modeled probabilistically as r k i ∼N (µ k i ,(σ k i ) 2 ), whereas the full configuration serves as a deterministic anchor as µ full i . (b) LUA aligns each subset mean µ k i with the full-modality anchor while enabling σ k i to reflect their discre… view at source ↗

**Figure 2.** Qualitative results on BraTS 2020. Our model produces improved segmentations over RFNet [8] and DC-Seg [14] across various configurations. (Region: ET (blue) ⊂ TC (blue, red) ⊂ WT (blue, red, green), Sample: “HG_BraTS20_Training_357”) T1 Flair T1c Ground truth Modalities Uncertainty T1+Flair T1+Flair+T1c +T1 T1 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Visualization of input modalities and ground-truth mask, with trained uncertainty overlaid on T1. Uncertainty, initially higher within tumor regions, decreases as additional modalities are incorporated. (Sample: “HG_BraTS20_Training_045”) Our framework was trained for 800 epochs using the Adam optimizer with a learning rate of 2×10−4 and a batch size of 2, where α=0.01 and β=5. 4 Results and Analysis 4.1 Performanc… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Variance magnitude ( ) and summed test DSC across tumor subregions ( ) for each modality configuration on BraTS 2020. • and ◦ indicate observed and missing modalities (Flair, T1, T1c, T2). Higher uncertainty correlates with lower performance. The shaded regions highlig…

**Figure 5.** Figure 5: Effect of variance magnitude on gradient behavior under controlled scaling. 4.2 Model Behavior Analyses Ablation Study. We analyze the contribution of each component over the deterministic baseline (i.e., RFNet [8]) on BraTS 2020 (Tab. 3). While adopting probabilistic…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 2 canonical work pages

[1]

In: ISBI

Baek, S., Sim, J., Dere, M., et al.: Modality-agnostic style transfer for holistic feature imputation. In: ISBI. pp. 1–5. IEEE (2024)

2024
[2]

In: MICCAI

Baek, S., Sim, J., Wu, G., et al.: Ocl: Ordinal contrastive learning for imputating features with progressive labels. In: MICCAI. pp. 334–344. Springer (2024)

2024
[3]

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Baid, U., Ghodasara, S., Mohan, S., et al.: The rsna-asnr-miccai brats 2021 bench- mark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

work page Pith review arXiv 2021
[4]

Scientific data 9(1), 453 (2022)

Bakas, S., Sako, C., Akbari, H., et al.: The university of pennsylvania glioblastoma (upenn-gbm) cohort: advanced mri, clinical, genomics, & radiomics. Scientific data 9(1), 453 (2022)

2022
[5]

JMI7(3), 031505– 031505 (2020)

Bakas, S., Shukla, G., Akbari, H., et al.: Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (mri): advanced radiomic features may compensate for lack of advanced mri modalities. JMI7(3), 031505– 031505 (2020)

2020
[6]

PLoS One12(5), e0177135 (2017)

Blystad, I., Warntjes, J.M., Smedby, Ö., et al.: Quantitative mri for analysis of peritumoral edema in malignant gliomas. PLoS One12(5), e0177135 (2017)

2017
[7]

Radiology299(2), 313–323 (2021)

Conte, G.M., Weston, A.D., Vogelsang, D.C., et al.: Generative adversarial net- works to synthesize missing t1 and flair mri sequences for use in a multisequence brain tumor segmentation model. Radiology299(2), 313–323 (2021)

2021
[8]

In: ICCV

Ding, Y., Yu, X., Yang, Y.: Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In: ICCV. pp. 3975–3984 (2021)

2021

Show all 21 references

[9]

In: MICCAI

Dorent, R., Joutard, S., Modat, M., et al.: Hetero-modal variational encoder- decoder for joint modality completion and segmentation. In: MICCAI. pp. 74–82. Springer (2019) 10 S. Baek & J. Park et al

2019
[10]

Medical image analysis35, 18–31 (2017)

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural net- works. Medical image analysis35, 18–31 (2017)

2017
[11]

Procedia computer science102, 317–324 (2016)

Işın, A., Direkoğlu, C., Şah, M.: Review of mri-based brain tumor image segmenta- tion using deep learning methods. Procedia computer science102, 317–324 (2016)

2016
[12]

Medical image analysis 36, 61–78 (2017)

Kamnitsas, K., Ledig, C., Newcombe, V.F., et al.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis 36, 61–78 (2017)

2017
[13]

arXiv preprint arXiv:1312.6114 (2013)

Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

2013 arXiv
[14]

In: MICCAI

Li, H., Li, Z., Mao, Y., et al.: Dc-seg: Disentangled contrastive learning for brain tumor segmentation with missing modalities. In: MICCAI. pp. 138–148. Springer (2025)

2025
[15]

In: AAAI

Liu, H., Wei, D., Lu, D., et al.: M3ae: multimodal representation learning for brain tumor segmentation with missing modalities. In: AAAI. vol. 37, pp. 1657–1665 (2023)

2023
[16]

Ecology 26, 297–302 (1945)

Lr, D.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)

1945
[17]

TMI34(10), 1993–2024 (2014)

Menze, B.H., Jakab, d., Bauer, S., et al.: The multimodal brain tumor image seg- mentation benchmark (brats). TMI34(10), 1993–2024 (2014)

1993
[18]

TMI35(5), 1240–1251 (2016)

Pereira, S., Pinto, A., Alves, V., et al.: Brain tumor segmentation using convolu- tional neural networks in mri images. TMI35(5), 1240–1251 (2016)

2016
[19]

TMI39(4), 1170–1183 (2019)

Sharma,A.,Hamarneh,G.:Missingmripulsesequencesynthesisusingmulti-modal generative adversarial network. TMI39(4), 1170–1183 (2019)

2019
[20]

NeurIPS 30(2017)

Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. NeurIPS 30(2017)

2017
[21]

In: MICCAI

Zhang, Y., He, N., Yang, J., et al.: mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In: MICCAI. pp. 107–117. Springer (2022)

2022

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[1] [1]

In: ISBI

Baek, S., Sim, J., Dere, M., et al.: Modality-agnostic style transfer for holistic feature imputation. In: ISBI. pp. 1–5. IEEE (2024)

2024

[2] [2]

In: MICCAI

Baek, S., Sim, J., Wu, G., et al.: Ocl: Ordinal contrastive learning for imputating features with progressive labels. In: MICCAI. pp. 334–344. Springer (2024)

2024

[3] [3]

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Baid, U., Ghodasara, S., Mohan, S., et al.: The rsna-asnr-miccai brats 2021 bench- mark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)

work page Pith review arXiv 2021

[4] [4]

Scientific data 9(1), 453 (2022)

Bakas, S., Sako, C., Akbari, H., et al.: The university of pennsylvania glioblastoma (upenn-gbm) cohort: advanced mri, clinical, genomics, & radiomics. Scientific data 9(1), 453 (2022)

2022

[5] [5]

JMI7(3), 031505– 031505 (2020)

Bakas, S., Shukla, G., Akbari, H., et al.: Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (mri): advanced radiomic features may compensate for lack of advanced mri modalities. JMI7(3), 031505– 031505 (2020)

2020

[6] [6]

PLoS One12(5), e0177135 (2017)

Blystad, I., Warntjes, J.M., Smedby, Ö., et al.: Quantitative mri for analysis of peritumoral edema in malignant gliomas. PLoS One12(5), e0177135 (2017)

2017

[7] [7]

Radiology299(2), 313–323 (2021)

Conte, G.M., Weston, A.D., Vogelsang, D.C., et al.: Generative adversarial net- works to synthesize missing t1 and flair mri sequences for use in a multisequence brain tumor segmentation model. Radiology299(2), 313–323 (2021)

2021

[8] [8]

In: ICCV

Ding, Y., Yu, X., Yang, Y.: Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In: ICCV. pp. 3975–3984 (2021)

2021