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REVIEW 5 major objections 6 minor 89 references

Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that a contrastively learned, group-aware metric makes slice-based active learning for 3D medical segmentation substantially more efficient at annotation budgets of 2-5%.

desk verdict Useful idea and thorough experiments, but the headline ACDC results are selected on the test set and the main/appendix numbers disagree, so the paper needs a clean revision before the claims hold. read the letter →

arxiv 2411.15763 v1 pith:2J3OWCZ4 submitted 2024-11-24 cs.CV cs.LG

Arvind Murari Vepa , Zukang Yang , Andrew Choi , Jungseock Joo , Fabien Scalzo , Yizhou Sun This is my paper · ORCID
classification cs.CVcs.LG
keywords activelearning3Dmedicalsegmentationcoresetcontrastivemetricweaksupervisionslice-basedannotationdeep
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

Deep learning segmentation of 3D medical volumes is annotation-hungry, and active learning is meant to cut that cost. This paper argues that the standard Coreset strategy, which picks diverse slices by Euclidean distance, picks the wrong slices because that distance ignores medically meaningful structure. The authors replace it with a learned metric $d_\phi(x_1,x_2)=\ell_2(g_\phi(x_1),g_\phi(x_2))$ trained by a Group-based Contrastive Learning loss that pulls slices from the same patient, volume, or adjacent-slice group together. They report that this group-aware Coreset beats existing active learning methods on both weak and full annotations at low budgets, for example 52.3 versus 45.2 DICE at 2% weak supervision on ACDC. If correct, the method would let clinicians train usable 3D segmentation models from a tiny fraction of labeled slices.

What carries the argument

The load-bearing object is the learned metric $d_\phi(x_1,x_2)=\ell_2(g_\phi(x_1),g_\phi(x_2))$, produced by a SimCLR-style encoder trained with Group-based Contrastive Learning. The group loss modifies NT-Xent so that slices from the same patient, volume, or adjacent-slice group are treated as positives while non-group slices from the same patient are excluded from the denominator, allowing several group losses to be summed without cancelling. This metric replaces the Euclidean distance in the Coreset objective $\arg\min_{\Delta s} \max_{x_1\in D}\min_{x_2\in s\cup\Delta s} d_\phi(x_1,x_2)$, solved by K-Center Greedy, a 2-approximation algorithm. The paper adapts the Coreset radius bound, assuming the loss and label function are Lipschitz in $d_\phi$ with zero training and generalization error, to justify the choice.

What would settle it

On the ACDC dataset, compare the 2% weak-supervision DICE of the full method (52.3 in the main table) against the same pipeline with the metric replaced by raw-pixel Euclidean distance and by an embedding trained on randomly shuffled group labels. If either replacement retains the same gap over vanilla Coreset (45.2), the group-aware metric itself is not the cause of the gain; additionally, measuring the ratio $|L(\hat y_1,y_1)-L(\hat y_2,y_2)|/d_\phi(x_1,x_2)$ on selected pairs would test the proportionality assumption directly.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that injecting inherent data groups into metric learning fixes the main weakness of Coreset-based active learning for slice-based 3D segmentation. The paper proposes the loss $L_{\text{contrastive}} = L_{\text{NT-Xent}} + \lambda_1 L_{\text{patient}} + \lambda_2 L_{\text{volume}} + \lambda_3 L_{\text{slice}}$, where each group loss is an NT-Xent-style contrastive term over slices sharing that grouping, with a batch sampler that guarantees group mates appear in each batch. The learned encoder $g_\phi$ defines the Coreset distance, and K-Center Greedy then selects slices so that every unlabeled slice is close to some selected slice in that metric. Across ACDC, MS-CMR, CHAOS, and DAVIS, the paper reports that this approach achieves the highest or near-highest DICE at low annotation budgets under both weak and full supervision, with the best ablation combining patient, volume, and NT-Xent losses.

Load-bearing premise

The method assumes that Euclidean distance in the learned embedding is a faithful proxy for how much a slice will improve the segmentation model, and the formal bound additionally assumes that small changes in the learned distance guarantee small changes in the loss and label function, with the model perfectly fitting the training data.

Editorial extensions

If this is right

  • At annotation budgets of 2-5%, group-aware Coreset gives the largest reported gains; for example, ACDC weak supervision jumps from 45.2 DICE with vanilla Coreset to 52.3 DICE at 2% annotation.
  • Combining active learning with weak supervision works: the method is competitive or best in both weak and full annotation settings, suggesting scribble-based active learning is a viable cost cut.
  • Slice-based selection with the learned metric outperforms volume-based random sampling for equal annotation time, per the paper's Figure 2.
  • The grouping idea transfers to video: treating videos as volumes and frames as slices yields strong results on DAVIS, so the method is not confined to medical images.
  • With pre-trained segmentation backbones, the method still improves mean DICE over baselines on ACDC, CHAOS, and DAVIS, though the gains shrink.
  • Inference: the same group-contrastive encoder could be trained once and reused across active learning rounds without retraining, which would cut the reported 24-hour ACDC experiment cost; the paper does not test this.
  • Inference: patient, volume, and slice groupings are proxies for covariate shift, so at a deployment site with different anatomy distributions the metric's diversity may miss task-relevant slices; a testable extension would build groups from clustering the embedding itself.
  • Inference: the ablation ranking, with volume group best and adjacent-slice group worst, suggests the diversity signal matters more than local redundancy; one could test this by weighting group losses by measured within-group variance rather than tuning weights.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper proposes Group-based Contrastive Learning (GCL) combined with the Coreset algorithm for slice-based active learning in 3D medical segmentation. The contrastive encoder is trained with a sum of NT-Xent and group-contrastive losses defined over patient, volume, and adjacent-slice groupings; the learned embedding distance is then used by K-Center Greedy Coreset to select slices for annotation. The authors evaluate on ACDC, CHAOS, MS-CMR, and DAVIS under both weak (scribble) and full supervision, and report results from scratch and with pretrained encoders. They claim superior performance over existing active learning methods at low annotation budgets, and additionally compare weak-supervised slice-based AL with fully-supervised volume-based AL in terms of annotation time.

Significance. If the empirical claims held, the paper would make a useful contribution: combining metric learning with Coreset for slice-based AL in medical segmentation is a sensible direction, and the use of inherent data groupings (patient/volume/slice) is a reasonable inductive bias. The paper ships source code, reports bootstrap standard errors in the appendix, and includes a t-SNE visualization and an ablation over loss combinations. The main weakness is that the central empirical claim is currently not established because the loss weights were apparently selected on the evaluation set, and the reported ACDC numbers are internally inconsistent between the main text and the appendix. The theoretical guarantee in Appendix B rests on unverified Lipschitz and zero-error assumptions, so the contribution's practical value depends on the empirical evidence, which needs substantial clarification.

major comments (5)
  1. [Appendix A and Section 4.6] The selection of contrastive loss weights is performed on the evaluation set rather than on a held-out validation split. Appendix A states that in the ablation study on ACDC the authors tried different combinations of weights and "reported the best results," then "utilized the best loss/weight combination for our ACDC experiments." No validation split is described anywhere in Section 4.2. Because the baseline methods are reported at their default settings, the "Ours" row in Table 1 is effectively the maximum over a hyperparameter grid on the test set. This is a form of test-set fitting and can inflate the reported margins, undermining the abstract's claim that the approach "surpasses existing active learning techniques." The authors should either describe a proper validation procedure or re-run the comparison with weights selected on a validation split.
  2. [Table 1 versus Appendix D Table 5] The ACDC weakly-supervised results are inconsistent between the two tables. Table 1 reports Ours = 52.3/59.8/73.3/76.1 at 2–5%, while Table 5 in Appendix D reports Ours = 55.6/61.4/73.7/77.5 for the same setting. Both tables are presented as results of the same method and no explanation is given for the discrepancy. If the lower values are correct, the claimed advantage over Coreset is smaller than stated (e.g., 52.3 vs 45.2 at 2% instead of 55.6 vs 45.2); if the higher values are correct, the main text underreports the method's performance. Either way, the reader cannot determine which result is authoritative, and this undermines the central quantitative claim in Section 4.3.
  3. [Tables 1 and 2 and Section 4.5] The comparison is incomplete for several baselines the paper claims to evaluate. Section 4.2 lists VAAL, TypiClust, and CoreGCN as comparison methods, but VAAL and TypiClust are absent from the MS-CMR and CHAOS panels of Table 1, and all three are absent from the DAVIS results in Table 2. The summary in Section 4.5 says "our method achieves the best performance on 21 out of 27 comparison points" without accounting for these missing entries. The authors should clarify whether these baselines were run on those datasets and, if so, report the results; otherwise, the claim of a comprehensive comparison is not supported.
  4. [Table 2 and Abstract] The abstract and Section 4.3 claim the method "surpasses existing active learning techniques" on weak and full annotations, but Table 2 shows that on DAVIS at 30% and 40% annotation, Random sampling achieves higher DICE (47.4 and 48.5) than Ours (45.5 and 46.6). Similarly, in Table 3 the pretrained results on CHAOS (Ours 95.2 vs Coreset 95.1) and DAVIS (Ours 75.1 vs Stochastic Batches 75.1) are effectively ties. The claim of universal superiority is therefore too strong as stated and should be qualified to the low-budget regimes where the advantage actually appears.
  5. [Appendix B and Section 3.2] The theoretical bound in Theorem 1 assumes the loss function L and the segmentation function eta_c are Lipschitz continuous with respect to the learned metric d_phi, and it assumes both zero training error and zero generalization error. These assumptions are not verified experimentally or by construction, and the paper provides no evidence that the self-supervised contrastive embedding aligns with segmentation-task difficulty. As a result, the Coreset guarantee in Equation (2) is not established for the actual learned d_phi. This does not invalidate the empirical approach, but it should be stated more cautiously, and the authors should at least discuss whether the Lipschitz constants can be bounded in practice.
minor comments (6)
  1. [Section 4.1 and Table 8] The DAVIS dataset description says the 2016 train set was used and the 2016 val set was split into val and test, but it does not specify how many objects/videos appear in each split beyond the totals; please state the exact number of training and test videos used in the DAVIS experiments.
  2. [Section 4.4 and Figure 2] Figure 2's annotation-time comparison relies on the assumption that scribble annotation is 15x faster than full mask annotation, but no sensitivity analysis is given for this multiplier; a brief discussion of how the comparison changes with a different multiplier would strengthen the claim.
  3. [Section 4.6 and Table 4] The ablation table does not report standard errors, making it hard to judge whether the differences between loss combinations (e.g., 65.4 vs 64.1) are significant; please add error bars or state the number of seeds used for each ablation row.
  4. [Section 1] The paper claims to be "the first work to integrate deep metric learning with Coreset during active learning for 3D medical segmentation," but prior work on unsupervised Coreset selection with contrastive learning (e.g., [48] and [49]) exists; please soften the novelty claim or clearly distinguish the contribution from those methods.
  5. [Section 2] The related work section cites [42] for Random Sampling, but [42] is a paper on robust active learning, not a canonical random sampling reference; please provide an appropriate citation for uniform random selection.
  6. [Appendix D and Tables 5-11] The bootstrap standard errors in the appendix are reported as two standard deviations, but the main text never defines this convention; please define the error bars when they are first introduced.

Circularity Check

1 steps flagged · score 6.0 of 10

ACDC low-budget superiority is partly a test-set-selected hyperparameter result, not an independent prediction; the learned metric itself is not definitionally circular.

  1. fitted input called prediction [Appendix A (Loss weights), Table 4 caption, and Table 1 ACDC rows]
    "In the ablation study on the ACDC dataset, for the experiments with multiple group contrastive losses, we tried different combinations of weights for the group contrastive losses and reported the best results. ... We utilized the best loss/weight combination for our ACDC experiments."

    Table 4 is captioned 'Ablation study based on the mean DICE scores for the 2-5% weak annotation datapoint', and its best row (NT-Xent + patient + volume, mDICE 65.4) is exactly the mean of the Table 1 ACDC 'Ours' weakly-supervised 2-5% entries (52.3 + 59.8 + 73.3 + 76.1)/4 = 65.4. Thus the loss weights used for the reported ACDC result were chosen by maximizing the same reported DICE values that are then presented as the method's prediction. The 'Ours' row is a post-selected maximum over a small weight grid on the evaluation metric, while baselines are reported without this selection; the claimed low-budget ACDC advantage is therefore partly forced by this construction.

full rationale

The core metric-learning contribution is not circular: g_phi is trained with self-supervised group contrastive losses on unlabeled slice groupings, and the resulting d_phi is then used in the standard Coreset/k-center objective; no target segmentation label is used to define the metric. The Appendix B bound is inherited from Sener and Savarese, and its Lipschitz and zero-error assumptions are unverified, which is an assumption risk rather than a circular reduction. There is no load-bearing self-citation or imported uniqueness theorem. The significant issue is the fitted-input-called-prediction pattern in Appendix A: the ACDC loss weights were selected by reporting the best ablation result on the evaluation metric, and that best setting produced Table 1's headline numbers. This is compounded by an internal inconsistency in which the same ACDC weak-supervision setting is reported as 52.3 in Table 1 but 55.6 in Table 5, so the claimed superiority is unstable. Because the central empirical claim is partly a test-set-selected maximum rather than an independent prediction, a partial-circularity score of 6 is appropriate.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central method depends on several unverified assumptions: the equivalence of volume and slice level losses, zero training/generalization error, and Lipschitz continuity in the learned metric. The loss weights are free parameters tuned on the evaluation set. No new entities are introduced.

free parameters (2)
  • Contrastive loss weights (lambda1, lambda2, lambda3) = Best tuple selected from a small grid; exact values not printed in the text (Appendix A)
    The weight combination was chosen by reporting the best DICE on ACDC 2-5% weak annotation, which is effectively fitting to the evaluation set.
  • Choice of group losses (patient, volume, slice) and inclusion of NT-Xent = Patient+volume+NT-Xent for ACDC full; patient only for pretrained; two configurations tested on CHAOS/MS-CMR/DAVIS and…
    Selected via ablation on ACDC test performance; for other datasets, two configurations were tested and the better one is reported without a described validation split.
assumptions (4)
  • domain assumption Volume-level and slice-level loss expectations are equal (Appendix B).
    Required for the theoretical bound; stated without proof.
  • ad hoc to paper Generalization error and training error are zero (Appendix B).
    Used to drop terms and obtain the stated bound. This is not a standard assumption and is stated without justification.
  • ad hoc to paper Loss function and segmentation function are Lipschitz continuous with respect to the learned metric d_phi (Appendix B, Theorem 1).
    No evidence that the contrastive embedding satisfies this property.
  • domain assumption Self-supervised contrastive features capture task-relevant diversity for the segmentation model.
    The entire method relies on this; it is only indirectly validated by end-task results, not by a direct check.

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Cite this review

Pith. "Pith review of Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation." pith.science (2026). https://pith.science/paper/2J3OWCZ4

@misc{pith2026241115763,
  author       = {Pith},
  title        = {Pith review of: Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2J3OWCZ4}},
  note         = {Machine review of arXiv:2411.15763}
}
read the original abstract

Deep learning has seen remarkable advancements in machine learning, yet it often demands extensive annotated data. Tasks like 3D semantic segmentation impose a substantial annotation burden, especially in domains like medicine, where expert annotations drive up the cost. Active learning (AL) holds great potential to alleviate this annotation burden in 3D medical segmentation. The majority of existing AL methods, however, are not tailored to the medical domain. While weakly-supervised methods have been explored to reduce annotation burden, the fusion of AL with weak supervision remains unexplored, despite its potential to significantly reduce annotation costs. Additionally, there is little focus on slice-based AL for 3D segmentation, which can also significantly reduce costs in comparison to conventional volume-based AL. This paper introduces a novel metric learning method for Coreset to perform slice-based active learning in 3D medical segmentation. By merging contrastive learning with inherent data groupings in medical imaging, we learn a metric that emphasizes the relevant differences in samples for training 3D medical segmentation models. We perform comprehensive evaluations using both weak and full annotations across four datasets (medical and non-medical). Our findings demonstrate that our approach surpasses existing active learning techniques on both weak and full annotations and obtains superior performance with low-annotation budgets which is crucial in medical imaging. Source code for this project is available in the supplementary materials and on GitHub: https://github.com/arvindmvepa/al-seg.

Figures

Figures reproduced from arXiv: 2411.15763 by the authors.

Figure 1
Figure 1. Overview of our active learning pipeline [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Describes the relationship between model performance and annotation time for our method [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison of our method, CoreGCN, and Coreset. Blue indicates agreement [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: t-SNE visualization of dataset clusters generated by different [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Overview of the batch sampler for Group-based Contrastive Learning [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.