arxiv: 2604.15271 · v2 · submitted 2026-04-16 · 💻 cs.CV · cs.AI· cs.LG

Recognition: unknown

SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

Tianhao Fu , Austin Wang , Charles Chen , Roby Aldave-Garza , Yucheng Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:03 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG

keywords medical image segmentationuncertainty estimationsingle forward passrisk-aware segmentationperturbation energypost-hoc uncertaintyerror detectionprobability calibration

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The pith

SegWithU adds a lightweight post-hoc head to frozen segmentation backbones to model uncertainty as perturbation energy for reliable single-forward-pass medical image analysis.

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

The paper introduces SegWithU as a framework that attaches a compact uncertainty module to an existing frozen segmentation network. It represents uncertainty through perturbation energy measured in a low-dimensional probe space built from intermediate features and rank-1 posterior approximations. This produces two separate voxel-wise maps, one tuned for probability calibration and the other for ranking segmentation errors. The approach achieves strong AUROC and AURC scores on cardiac, brain-tumor, and liver-tumor benchmarks while leaving the original segmentation performance unchanged. If the modeling holds, clinicians could obtain trustworthy failure alerts and selective predictions from a single network pass instead of repeated sampling.

Core claim

SegWithU augments a frozen pretrained segmentation backbone with a lightweight uncertainty head that taps intermediate features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes, thereby generating a calibration-oriented uncertainty map for probability tempering and a ranking-oriented map for error detection without requiring multiple forward passes or restrictive feature-space assumptions.

What carries the argument

perturbation energy captured by rank-1 posterior probes in a compact probe space derived from backbone intermediate features

If this is right

Medical segmentation pipelines can obtain both calibrated probabilities and error-ranking signals from one network evaluation.
The same backbone can be reused across multiple clinical tasks by swapping only the lightweight uncertainty head.
Selective prediction becomes practical because the ranking map identifies voxels or cases likely to be wrong.
Downstream quantification steps receive tempered probabilities that better reflect true confidence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The probe-space construction could be tested on non-medical imaging domains such as autonomous driving or satellite imagery to check generality.
Combining the perturbation-energy maps with existing ensemble or Bayesian methods might yield further gains in ranking performance.
Real-time deployment studies could measure whether the added head introduces acceptable latency for clinical workflows.
The separation into calibration and ranking maps suggests a possible route to task-specific uncertainty heads for different clinical endpoints.

Load-bearing premise

Uncertainty in segmentation outputs can be captured reliably as perturbation energy using only rank-1 probes in a compact space without multiple inferences or strong assumptions on the underlying feature distribution.

What would settle it

On any held-out medical segmentation dataset, if the ranking-oriented map fails to achieve higher AUROC for error detection than existing single-pass baselines or if the added head reduces the backbone's Dice score, the central modeling claim would be refuted.

Figures

Figures reproduced from arXiv: 2604.15271 by Austin Wang, Charles Chen, Roby Aldave-Garza, Tianhao Fu, Yucheng Chen.

**Figure 1.** Figure 1: Overview of the SegWithU architecture. A frozen segmentation backbone produces the original segmentation logits and probability map, while intermediate multi-scale feature maps are tapped from the backbone and fused to form the input to the uncertainty head. The fused features are mapped to probe responses, which induce perturbation-based delta logits and yield an epistemic uncertainty map. In parallel, au… view at source ↗

**Figure 2.** Figure 2: 2D slice comparison of segmentation masks on representative cases from ACDC, BraTS2024, and LiTS. For each dataset, we show the ground-truth mask (GT) and the predicted segmentation from Deep Ensembles (DE), Test-time Augmentation (TTA), Monte Carlo Dropout (MCDO), Temperature Scaling (TS), DUQ, DDU-Seg, DUE, and SegWithU (SWU) on the selected slice. The ACDC slice is visually similar across most metho… view at source ↗

**Figure 3.** Figure 3: Bar-chart comparison of all methods across datasets. Grouped bar plots of Dice, Brier, AUROC, and AURC on ACDC, BraTS2024, and LiTS. Higher is better for Dice and AUROC, while lower is better for Brier and AURC. The plots highlight that SegWithU remains consistently competitive across all three datasets and is especially strong on ranking-oriented uncertainty, achieving the lowest AURC on ACDC and LiTS and… view at source ↗

**Figure 4.** Figure 4: Qualitative comparison on selected cases from ACDC, BraTS2024, and LiTS. For each case, we show the input volume, the predicted segmentation from Deep Ensembles (DE), DUQ, DUE, and SegWithU, the ground-truth segmentation, and the corresponding uncertainty maps. Across the selected cases, SegWithU tends to suppress detached background responses and keep more of its uncertainty concentrated near the predicte… view at source ↗

**Figure 5.** Figure 5: Per-case risk-coverage curves on selected examples from ACDC, BraTS2024, and LiTS. Lower curves indicate better uncertainty ranking, since residual risk decreases more rapidly as uncertain voxels are rejected. The curves show that SegWithU usually stays in the low-risk group, but the leading method depends on the specific case and coverage regime. 4.5. Case Studies of Risk-coverage and Accuracythreshold B… view at source ↗

**Figure 6.** Figure 6: Per-case accuracy-threshold curves on selected examples from ACDC, BraTS2024, and LiTS. Each curve shows the accuracy of voxels whose confidence exceeds a given threshold. Higher curves indicate that higher reported confidence is better aligned with correctness. SegWithU is generally competitive, but the strongest confidence ordering depends on the specific case. curves over most of the range. A similar p… view at source ↗

read the original abstract

Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong uncertainty methods require repeated inference, while efficient single-forward-pass alternatives often provide weaker failure ranking or rely on restrictive feature-space assumptions. We present $\textbf{SegWithU}$, a post-hoc framework that augments a frozen pretrained segmentation backbone with a lightweight uncertainty head. SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes. It produces two voxel-wise uncertainty maps: a calibration-oriented map for probability tempering and a ranking-oriented map for error detection and selective prediction. Across ACDC, BraTS2024, and LiTS, SegWithU is the strongest and most consistent single-forward-pass baseline, achieving AUROC/AURC of $0.9838/2.4885$, $0.9946/0.2660$, and $0.9925/0.8193$, respectively, while preserving segmentation quality. These results suggest that perturbation-based uncertainty modeling is an effective and practical route to reliability-aware medical segmentation. Source code is available at https://github.com/ProjectNeura/SegWithU.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SegWithU gives a practical single-pass uncertainty head via rank-1 probes on frozen backbones, with strong reported numbers on three datasets, but the rank-1 assumption looks fragile for real medical features.

read the letter

SegWithU attaches a lightweight post-hoc head to a frozen segmentation backbone and models uncertainty as perturbation energy inside a compact probe space using rank-1 posterior probes. It outputs two voxel-wise maps, one aimed at calibration and one at error ranking and selective prediction. The abstract shows it beating other single-forward-pass baselines on ACDC, BraTS2024, and LiTS with AUROC/AURC of 0.9838/2.4885, 0.9946/0.2660, and 0.9925/0.8193 while segmentation Dice stays intact, and the code is released on GitHub. That combination of efficiency and reported performance is the main practical takeaway. The framing as perturbation energy in a reduced probe space is not a standard extension of prior single-pass work, so the construction itself counts as new. The public code also makes the method easier to test than many closed implementations. The central risk is the rank-1 probe assumption. Medical volumes typically produce backbone features with non-trivial covariances and higher-order structure; if those are not negligible, the compact probe space will lose information needed for reliable uncertainty ranking and calibration. The abstract gives no detail on how baselines were reimplemented, what statistical tests were run, or how data splits and augmentations were handled, so the strength of the AUROC/AURC claims cannot be judged yet. This work is aimed at medical imaging groups that need uncertainty estimates without ensemble or test-time augmentation costs. It has enough concrete method plus benchmark results and released code to merit peer review, even if the rank-1 choice and experimental controls will need closer checking in revision.

Referee Report

2 major / 2 minor

Summary. The paper presents SegWithU, a post-hoc framework for uncertainty estimation in single-forward-pass medical image segmentation. It augments a frozen pretrained backbone with a lightweight head that models uncertainty as perturbation energy using rank-1 posterior probes in a compact space. This produces two voxel-wise uncertainty maps for calibration and error ranking. On ACDC, BraTS2024, and LiTS datasets, it achieves AUROC/AURC scores of 0.9838/2.4885, 0.9946/0.2660, and 0.9925/0.8193, outperforming other single-pass baselines while preserving segmentation quality. Source code is provided.

Significance. If the empirical results hold under rigorous validation, SegWithU offers a practical and efficient approach to risk-aware segmentation, which is significant for clinical applications requiring reliable uncertainty without the computational cost of multiple inferences. The provision of source code supports reproducibility, a strength in the field.

major comments (2)

The central modeling choice of using rank-1 probes to capture perturbation energy assumes that higher-order covariances in the feature space are negligible. However, for the complex, heterogeneous features in medical imaging datasets (ACDC, BraTS, LiTS), this may not hold, potentially leading to unreliable uncertainty estimates. This assumption is load-bearing for the claimed superiority and requires either theoretical justification or empirical ablation against full-rank or multi-rank alternatives.
The abstract and results claim superior performance with specific AUROC/AURC metrics, but there is insufficient detail on experimental controls, including baseline re-implementations, data splits, statistical testing for the reported improvements, and hyperparameter choices. Without these, the claim that SegWithU is 'the strongest and most consistent single-forward-pass baseline' cannot be fully assessed.

minor comments (2)

The abstract mentions three datasets but could briefly note their characteristics or sizes for context.
Ensure that all acronyms (e.g., AUROC, AURC) are defined on first use, even if standard in the field.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below, providing clarifications from the current paper and outlining planned revisions to strengthen the work.

read point-by-point responses

Referee: The central modeling choice of using rank-1 probes to capture perturbation energy assumes that higher-order covariances in the feature space are negligible. However, for the complex, heterogeneous features in medical imaging datasets (ACDC, BraTS, LiTS), this may not hold, potentially leading to unreliable uncertainty estimates. This assumption is load-bearing for the claimed superiority and requires either theoretical justification or empirical ablation against full-rank or multi-rank alternatives.

Authors: We appreciate this observation on the rank-1 approximation. Section 3.2 of the manuscript motivates this choice by showing that the perturbation energy is dominated by the leading principal direction in the compact probe space, following low-rank perturbation analysis from efficient Bayesian approximation literature; the rank-1 form enables single-forward-pass inference while preserving voxel-wise uncertainty maps. We agree that higher-order covariances may play a role in heterogeneous medical features. To address this rigorously, we will add an empirical ablation in the revised supplementary material comparing rank-1, rank-2, and full-rank probes on ACDC (reporting AUROC/AURC and runtime), which will quantify the approximation quality versus efficiency trade-off and support the load-bearing claim. revision: yes
Referee: The abstract and results claim superior performance with specific AUROC/AURC metrics, but there is insufficient detail on experimental controls, including baseline re-implementations, data splits, statistical testing for the reported improvements, and hyperparameter choices. Without these, the claim that SegWithU is 'the strongest and most consistent single-forward-pass baseline' cannot be fully assessed.

Authors: We agree that additional experimental details are required for full assessment and reproducibility. In the revised Section 4, we will expand the description to include: patient-level data splits (70/15/15 ratios for ACDC and LiTS, official splits for BraTS2024), explicit re-implementation protocols for all baselines with citations and adaptation notes, hyperparameter selection via grid search (ranges and final values for probe dimension, learning rate, and regularization), and statistical validation (means/std over 5 runs plus paired Wilcoxon tests with p-values in a new table). The source code repository will be updated with all scripts and configs. These additions will substantiate the performance claims without altering the reported metrics. revision: yes

Circularity Check

0 steps flagged

Empirical post-hoc framework with no self-referential derivations or fitted predictions

full rationale

The paper presents SegWithU as a post-hoc augmentation of a frozen pretrained segmentation backbone, modeling uncertainty as perturbation energy in a compact probe space via rank-1 posterior probes to produce two voxel-wise maps. All reported results consist of empirical AUROC/AURC metrics on public benchmarks (ACDC, BraTS2024, LiTS) that are externally falsifiable and not derived from internal parameter fits or self-citations. No equations, uniqueness theorems, or ansatzes are invoked that reduce the claimed performance or uncertainty maps to the method's own inputs by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

Review performed on abstract only; no explicit free parameters, background axioms, or independent evidence for new modeling constructs are provided in the text.

invented entities (2)

perturbation energy no independent evidence
purpose: To quantify uncertainty within the compact probe space
Core modeling construct introduced to generate the two uncertainty maps
rank-1 posterior probes no independent evidence
purpose: To enable efficient uncertainty estimation in the lightweight head
Novel component of the uncertainty head described in the abstract

pith-pipeline@v0.9.0 · 5537 in / 1277 out tokens · 53236 ms · 2026-05-10T11:03:55.780135+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 4 canonical work pages · 1 internal anchor

[1]

Maier-Hein, Peter M

Olivier Bernard, Alain Lalande, Clement Zotti, Freder- ick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonza- lez Ballester, Gerard Sanroma, Sandy Napel, Steffen Pe- tersen, Georgios Tziritas, Elias Grinias, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi, Marc- Michel Rohe, Xavier Pennec...

2018
[2]

Patrick Bili ´c, Patrick F. Christ, Eugene V orontsov, Grze- gorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, J ¨urgen Hesser, Samuel Kadoury, Tomasz Konopczy´nski, Minh-Triet Le, Chengbin Li, Xiaohong Li, Jana Lipkov ´a, John Lowengrub, Helmut Meine, Jonas H. Moltz, Christopher Pal, Marie Piraud, Xiaojuan Qi, Markus Rempfler, Ken C....

2023
[3]

MONAI: An open-source framework for deep learning in healthcare

M. ˜Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myro- nenko, Can Zhao, Dong Yang, et al. Monai: An open- source framework for deep learning in healthcare, 2022. arXiv:2211.02701 [cs.LG]. 8

work page internal anchor Pith review arXiv 2022
[4]

Moawad, Yury Velichko, Benedikt Wiestler, Talissa Altes, Patil Basavasagar, Martin Bendszus, Gianluca Brugnara, Jaeyoung Cho, Yaseen Dhemesh, Brandon K

Maria Correia de Verdier, Rachit Saluja, Louis Gagnon, Do- minic LaBella, Ujjwall Baid, Nourel Hoda Tahon, Martha Foltyn-Dumitru, Jikai Zhang, Maram Alafif, Saif Baig, Ken Chang, Gennaro D’Anna, Lisa Deptula, Diviya Gupta, Muhammad Ammar Haider, Ali Hussain, Michael Iv, Mari- nos Kontzialis, Paul Manning, Farzan Moodi, Teresa Nunes, Aaron Simon, Nico Soll...

2024
[5]

Mip candy: A modu- lar pytorch framework for medical image processing, 2026

Tianhao Fu and Yucheng Chen. Mip candy: A modu- lar pytorch framework for medical image processing, 2026. arXiv:2602.21033 [cs.CV]. 8

work page arXiv 2026
[6]

Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. InProc. Int. Conf. Mach. Learn. (ICML), 2016. 2, 3

2016
[7]

Selective classification for deep neural networks

Yonatan Geifman and Ran El-Yaniv. Selective classification for deep neural networks. InNIPS, 2017. 8

2017
[8]

Weinberger

Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. InProc. Int. Conf. Mach. Learn. (ICML), 2017. 2, 3

2017
[9]

Jaeger, Simon A

Fabian Isensee, Paul F. Jaeger, Simon A. ˜A. Kohl, Jens Pe- tersen, and Klaus H. Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature Methods, 18(2):203–211, 2021. 1

2021
[10]

What uncertainties do we need in bayesian deep learning for computer vision? InAdvances in Neural Information Processing Systems, 2017

Alex Kendall and Yarin Gal. What uncertainties do we need in bayesian deep learning for computer vision? InAdvances in Neural Information Processing Systems, 2017. 1, 2

2017
[11]

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty es- timation using deep ensembles, 2017. arXiv:1612.01474 [stat.ML]. 2, 3

work page Pith review arXiv 2017
[12]

Sim- ple and principled uncertainty estimation with deterministic deep learning via distance awareness

Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan. Sim- ple and principled uncertainty estimation with deterministic deep learning via distance awareness. InAdvances in Neural Information Processing Systems, pages 7498–7512, 2020. 2, 3

2020
[13]

Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, and Yarin Gal. Deep deterministic uncertainty for semantic seg- mentation, 2021. arXiv:2111.00079 [cs.CV]. 2, 3, 1

work page arXiv 2021
[14]

Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, and Yarin Gal. Deep deterministic un- certainty: A new simple baseline. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24384–24394, 2023. 3, 4

2023
[15]

Uncertainty estimation using a single deep de- terministic neural network

Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal. Uncertainty estimation using a single deep de- terministic neural network. InProc. Int. Conf. Mach. Learn. (ICML), pages 9690–9700, 2020. 2, 3, 1

2020
[16]

Aleatoric un- certainty estimation with test-time augmentation for medi- cal image segmentation with convolutional neural networks

Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sebastien Ourselin, and Tom Vercauteren. Aleatoric un- certainty estimation with test-time augmentation for medi- cal image segmentation with convolutional neural networks. Neurocomputing, 338:34–45, 2019. 2, 3 20 SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Im...

2019