pith. sign in

arxiv: 2502.09269 · v3 · submitted 2025-02-13 · 💻 cs.CV

Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation

Yiwei Liu , Liang Zhong , Lingyi Wen , Yuankai Wu This is my paper

Pith reviewed 2026-05-23 03:22 UTC · model grok-4.3

classification 💻 cs.CV
keywords uncertainty-based ensembleCMR semantic segmentationend-slice performanceglobal uncertaintyEnd CoefficientACDC datasetM&Ms datasetStreaming ensemble
0
0 comments X

The pith

An ensemble method weights cardiac segmentation models by global uncertainty to improve end-slice accuracy.

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

The paper addresses the problem of poor performance on end slices in ventricular segmentation of cardiac cine MRI sequences, despite good overall results. It extracts global uncertainty from the variance across multiple segmentations and uses this to weight classifiers within an ensemble learning framework called Streaming. This weighting balances performance across all slices and the difficult end slices. A new metric, the End Coefficient, is introduced to specifically measure end-slice accuracy. Experiments on the ACDC and M&Ms datasets demonstrate near state-of-the-art overall Dice scores with superior end-slice performance.

Core claim

By extracting global uncertainty from segmentation variance and using it to weight classifiers in the Streaming ensemble, the framework achieves near state-of-the-art Dice Similarity Coefficient on ACDC and M&Ms datasets while outperforming all models on end-slice performance, as measured by the End Coefficient, thereby improving patient-specific segmentation accuracy.

What carries the argument

The Streaming ensemble learning method, which weights classifiers using global uncertainty extracted from segmentation variance, together with the End Coefficient metric for quantifying end-slice accuracy.

If this is right

  • The method improves the reliability of derived clinical functional metrics from ventricular segmentations.
  • Patient-specific accuracy increases due to better handling of end slices.
  • The approach maintains competitive overall DSC while excelling on challenging slices.
  • Code is open-sourced for reproducibility and further use.

Where Pith is reading between the lines

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

  • This weighting strategy based on uncertainty could be adapted to other medical image segmentation tasks with boundary or edge challenges.
  • Further testing on additional datasets or modalities might reveal the generalizability of the global uncertainty approach.
  • The End Coefficient could become a standard metric for evaluating segmentation in sequential imaging data.

Load-bearing premise

That global uncertainty extracted from segmentation variance can be used effectively for weighting classifiers in the ensemble to balance overall and end-slice performance.

What would settle it

An experiment showing that the uncertainty-weighted Streaming ensemble does not outperform standard ensembles or individual models on end-slice Dice scores in the ACDC or M&Ms datasets.

Figures

Figures reproduced from arXiv: 2502.09269 by Liang Zhong, Lingyi Wen, Yiwei Liu, Yuankai Wu.

Figure 1
Figure 1. Figure 1: Visual differences between traditional ensemble learning and ours. 3 Methodology Ensemble Strategy We define superadditive among classifiers as follows: f({yˆi(x)}N ) ⪰ 1 N X N i=1 yˆi(x), yˆi(x) ∈ R + D×4×H×W , (1) where x is an arbitrary 3D frame from the 4D cardiac cine sequence sample space X . The ensemble method is denoted as f(·), with N classifiers. ˆyi(x) is the probabilities of the i-th classifie… view at source ↗
Figure 2
Figure 2. Figure 2: a) shows results from 1UNet + 1D3P, with the x-axis varying UNet’s weight. b) shows results from 2UNet, with the x-axis varying UNet 1’s weight [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Test FLOPs and parameters on a 3D frame, marker size shows parameter count. Computational Efficiency Ensemble learning often faces complexity issues. Compared to baselines on ACDC, ours achieves near-SOTA Average DSC with low parameters and FLOPs, thanks to zero attention, as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: UNet Trio is 3UNet (Uncertainty), and UNet i is one of its components. Solo means working individually. −1 and −2 are the last two slices, 0 and 1 the first two. We visualized a challenging sample from the ACDC testset in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Existing methods derive clinical functional metrics from ventricular semantic segmentation in cardiac cine sequences. While performing well on overall segmentation, they struggle with the end slices. To address this, we extract global uncertainty from segmentation variance and use it in our ensemble learning method, Streaming, for classifier weighting, balancing overall and end-slice performance. We introduce the End Coefficient (EC) to quantify end-slice accuracy. Experiments on ACDC and M\&Ms datasets show that our framework achieves near state-of-the-art Dice Similarity Coefficient (DSC) and outperforms all models on end-slice performance, improving patient-specific segmentation accuracy. We open-sourced our code on https://github.com/LEw1sin/Uncertainty-Ensemble.

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.

Referee Report

2 major / 0 minor

Summary. The paper proposes extracting global uncertainty from segmentation variance to weight classifiers in a 'Streaming' ensemble method for cardiac cine MR semantic segmentation. This is intended to balance overall Dice performance with improved accuracy on end slices, which are clinically important. The authors introduce an 'End Coefficient' (EC) metric to quantify end-slice performance and report near-SOTA DSC on ACDC and M&Ms while outperforming baselines on end slices; code is open-sourced.

Significance. If the central mechanism holds, the work could meaningfully improve patient-specific segmentation reliability for downstream clinical metrics derived from ventricular segmentations. The open-sourced code supports reproducibility, which is a clear strength.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments: the central claim that uncertainty-driven weighting in Streaming produces end-slice gains (while preserving near-SOTA DSC) is not supported by any ablation that isolates the uncertainty signal against a variance-agnostic ensemble or per-slice uncertainty baseline; without this, it remains possible that gains arise from ensembling itself or dataset properties.
  2. [Abstract] Abstract: the statements of 'near state-of-the-art DSC' and 'outperforms all models on end-slice performance' lack any reported baseline numbers, statistical tests, or experimental-setup details (e.g., number of runs, cross-validation, significance thresholds), limiting evaluation of the quantitative claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments: the central claim that uncertainty-driven weighting in Streaming produces end-slice gains (while preserving near-SOTA DSC) is not supported by any ablation that isolates the uncertainty signal against a variance-agnostic ensemble or per-slice uncertainty baseline; without this, it remains possible that gains arise from ensembling itself or dataset properties.

    Authors: We agree that an explicit ablation isolating the uncertainty weighting mechanism would strengthen the central claim. In the revised manuscript we will add experiments that compare the full Streaming ensemble (uncertainty-weighted) against (i) a variance-agnostic ensemble using uniform weights and (ii) a per-slice uncertainty baseline. These results will be reported in the Experiments section with the same evaluation protocol to demonstrate that the observed end-slice improvements are attributable to the proposed global uncertainty signal rather than ensembling alone. revision: yes

  2. Referee: [Abstract] Abstract: the statements of 'near state-of-the-art DSC' and 'outperforms all models on end-slice performance' lack any reported baseline numbers, statistical tests, or experimental-setup details (e.g., number of runs, cross-validation, significance thresholds), limiting evaluation of the quantitative claims.

    Authors: We acknowledge that the abstract would benefit from greater specificity. We will revise the abstract to include the key DSC values achieved on ACDC and M&Ms, indicate the number of independent runs performed, and note that cross-validation was used. Full statistical details and significance thresholds will remain in the Experiments section, but the abstract will now reference these elements to make the quantitative claims more immediately verifiable. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper's central mechanism extracts global uncertainty from segmentation variance and applies it to weight classifiers in the Streaming ensemble. This is a direct, standard use of variance-derived uncertainty for ensemble weighting with no equations or steps shown that reduce the claimed end-slice gains to fitted inputs by construction, self-definitional loops, or load-bearing self-citations. The introduction of the End Coefficient (EC) is a new metric definition rather than a renaming of a known result, and the reported DSC and end-slice improvements are presented as experimental outcomes on ACDC/M&Ms rather than predictions forced by the method's own parameters. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Limited information available from abstract; the paper introduces the Streaming method and End Coefficient as new elements without specifying numerical free parameters or external axioms beyond typical machine learning assumptions.

axioms (1)
  • domain assumption Segmentation variance across models provides a meaningful measure of uncertainty for weighting
    Central to the Streaming method as described in the abstract.
invented entities (2)
  • Streaming ensemble method no independent evidence
    purpose: To weight classifiers using uncertainty for better end-slice performance
    New method introduced in the paper.
  • End Coefficient (EC) no independent evidence
    purpose: To quantify end-slice accuracy
    Introduced to measure the specific performance aspect.

pith-pipeline@v0.9.0 · 5641 in / 1203 out tokens · 38055 ms · 2026-05-23T03:22:51.844372+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 2 internal anchors

  1. [1]

    Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37(11), 2514–2525 (2018)

    work page 2018

  2. [2]

    IEEE Transactions on Medical Imaging 40(12), 3543–3554 (2021)

    Campello, V.M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P.M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge. IEEE Transactions on Medical Imaging 40(12), 3543–3554 (2021)

    work page 2021

  3. [3]

    In: European conference on computer vision

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

    work page 2022

  4. [4]

    TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

    Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  5. [5]

    In: Statistical Atlases and Computational Models of the Heart

    Corral Acero, J., Sundaresan, V., Dinsdale, N., Grau, V., Jenkinson, M.: A 2- step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020, Held in...

    work page 2020

  6. [6]

    In: Statistical Atlases and Computational Models of the Heart

    Full, P.M., Isensee, F., J¨ ager, P.F., Maier-Hein, K.: Studying robustness of seman- tic segmentation under domain shift in cardiac mri. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th In- ternational Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected ...

    work page 2020

  7. [7]

    In: Statistical Atlases and Computational Models of the Heart

    Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-mri via time-series segmentation and domain specific features. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 201...

    work page 2017

  8. [8]

    European heart journal-cardiovascular imaging 23(4), 450–464 (2022)

    Ismail, T.F., Hua, A., Plein, S., D’Cruz, D.P., Fernando, M.M., Friedrich, M.G., Zellweger, M.J., Giorgetti, A., Caobelli, F., Haaf, P.: The role of cardiovascu- lar magnetic resonance in the evaluation of acute myocarditis and inflammatory cardiomyopathies in clinical practice—a comprehensive review. European heart journal-cardiovascular imaging 23(4), 4...

    work page 2022

  9. [9]

    Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

    Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  10. [10]

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

    work page 2017

  11. [11]

    In: Statistical Atlases and Computational Models of the Heart

    Kong, F., Shadden, S.C.: A generalizable deep-learning approach for cardiac mag- netic resonance image segmentation using image augmentation and attention u- net. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October 4...

    work page 2020

  12. [12]

    pp. 287–296. Springer (2021) 10 Yuankai Wu

    work page 2021

  13. [13]

    In: International conference on machine learning

    Li, Y., Gal, Y.: Dropout inference in bayesian neural networks with alpha- divergences. In: International conference on machine learning. pp. 2052–2061. PMLR (2017)

    work page 2052

  14. [14]

    In: Proceedings of the IEEE international conference on computer vision

    Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll´ ar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)

    work page 2017

  15. [15]

    Ieee Access 10, 66467–66480 (2022)

    M¨ uller, D., Soto-Rey, I., Kramer, F.: An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks. Ieee Access 10, 66467–66480 (2022)

    work page 2022

  16. [16]

    In: Statistical Atlases and Computational Models of the Heart

    Parre˜ no, M., Paredes, R., Albiol, A.: Deidentifying mri data domain by iterative backpropagation. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers 11. pp. 277–286. Springer (2021)

    work page 2020

  17. [17]

    In: Statistical Atlases and Computational Mod- els of the Heart

    Patravali, J., Jain, S., Chilamkurthy, S.: 2d-3d fully convolutional neural networks for cardiac mr segmentation. In: Statistical Atlases and Computational Mod- els of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers 8. p...

    work page 2017

  18. [18]

    In: Medical Imaging with Deep Learning

    Rahman, M.M., Marculescu, R.: Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image segmentation. In: Medical Imaging with Deep Learning. pp. 1526–1544. PMLR (2024)

    work page 2024

  19. [19]

    arXiv preprint arXiv:2402.02491 (2024)

    Ruan, J., Xiang, S.: Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491 (2024)

    work page arXiv 2024

  20. [20]

    In: Statistical Atlases and Computational Models of the Heart

    Saber, M., Abdelrauof, D., Elattar, M.: Multi-center, multi-vendor, and multi- disease cardiac image segmentation using scale-independent multi-gate unet. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Re...

    work page 2020

  21. [21]

    In: Medical Image Computing and Com- puter Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV 23

    Sun, J., Darbehani, F., Zaidi, M., Wang, B.: Saunet: Shape attentive u-net for in- terpretable medical image segmentation. In: Medical Image Computing and Com- puter Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV 23. pp. 797–806. Springer (2020)

    work page 2020

  22. [22]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

    Tragakis, A., Kaul, C., Murray-Smith, R., Husmeier, D.: The fully convolutional transformer for medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3660–3669 (2023)

    work page 2023

  23. [23]

    In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

    Yan, X., Tang, H., Sun, S., Ma, H., Kong, D., Xie, X.: After-unet: Axial fu- sion transformer unet for medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 3971–3981 (2022)

    work page 2022