arxiv: 2605.10165 · v1 · submitted 2026-05-11 · 💻 cs.CV · cs.AI

Recognition: no theorem link

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

Inhyuk Park , Doohyun Park

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:03 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords noisy label detectionstandardized losscross-validationlabel reliabilitymedical imagingfundus datasettask-agnostic

0 comments

The pith

Aggregating standardized cross-validation losses produces reliable sample-level scores for detecting noisy labels.

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

Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability. The paper proposes Standardized Loss Aggregation (SLA) to quantify label reliability by combining standardized fold-level validation losses across repeated cross-validation runs. This turns discrete counts of poor performance into a continuous score that includes both how often and how severely a sample underperforms. Experiments on a fundus dataset show SLA outperforms hard-counting baselines at every noise level and converges faster when noise is low. If the approach holds, it enables more efficient identification of samples needing re-annotation without depending on the downstream classification task.

Core claim

SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores. Experiments on a public fundus dataset demonstrate that SLA consistently outperforms the hard-counting baseline across all noise levels and converges substantially faster, especially under low noise ratios where subtle loss variations are informative. Samples with high SLA scores indicate potentially ambiguous or mislabeled cases.

What carries the argument

Standardized Loss Aggregation (SLA): the mechanism that normalizes each fold's validation loss and aggregates the results across runs to produce a continuous noisiness score.

If this is right

High SLA scores identify samples that are likely ambiguous or mislabeled and therefore worth re-annotating first.
Overall dataset quality improves for any downstream classification task once high-scoring samples are reviewed.
The method delivers stable rankings even when the fraction of noisy labels is small.
Convergence to usable scores occurs faster than with simple counting of bad folds.

Where Pith is reading between the lines

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

The same standardization step might apply to regression or detection tasks if their loss surfaces respond similarly to label errors.
Combining SLA scores with uncertainty estimates from the model itself could further reduce the number of samples sent for human review.
On very large datasets the repeated cross-validation requirement could be approximated by fewer but larger folds while preserving score stability.

Load-bearing premise

That differences in a sample's validation loss across cross-validation folds are driven mainly by label noise rather than by random model behavior or how the data splits are chosen.

What would settle it

A controlled test on a dataset with known clean labels where noise is artificially added only to selected samples; if the resulting SLA scores do not rise specifically and proportionally for those noised samples, the central claim fails.

read the original abstract

Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framework, Standardized Loss Aggregation (SLA), for detecting noisy labels at the sample level. SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores. Experiments on a public fundus dataset demonstrate that SLA consistently outperforms the hard-counting baseline across all noise levels and converges substantially faster, especially under low noise ratios where subtle loss variations are informative. Samples with high SLA scores indicate potentially ambiguous or mislabeled cases, guiding efficient re-annotation and improving dataset reliability for any classification task.

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

SLA turns hard-counting into a continuous standardized loss score for noisy labels, but the core assumption that CV loss variance mainly reflects label noise rather than stochasticity or splits is untested and likely the main weakness.

read the letter

The paper's central move is to replace discrete hard-counting of misclassifications across CV folds with a standardized aggregation of per-sample validation losses. This produces a continuous noisiness score that incorporates both how often and how badly a sample deviates. On the surface that is a clean generalization and could be useful when noise levels are low and subtle loss differences matter. The experiments on the public fundus dataset claim consistent gains over the hard-counting baseline plus faster convergence, which is the main empirical support offered. The task-agnostic framing is reasonable since the method only requires repeated k-fold runs and standard losses. That part is straightforward and worth having on record. The soft spot is exactly the one the stress-test raises. The formulation does not isolate label noise from random initialization, optimizer noise, batch order, or the particular train-val splits. If those factors drive most of the loss variation, high SLA scores will simply mark hard-to-fit clean samples and the claimed advantage disappears. The abstract gives no equations, no quantitative metrics, no error bars, and no protocol details on how many repeats or how they controlled for stochasticity, so it is impossible to tell whether the reported outperformance survives that check. Only one dataset is shown. This is the kind of incremental method that belongs in the noisy-label literature for medical imaging. A referee could usefully test whether the standardization actually adds robustness once the variance sources are controlled. I would send it to review rather than desk-reject; the idea is clear enough to be worth a proper evaluation even if the current evidence is thin.

Referee Report

2 major / 2 minor

Summary. The paper introduces Standardized Loss Aggregation (SLA), a task-agnostic framework for per-sample noisy label detection. SLA standardizes and aggregates validation losses observed for each sample across repeated k-fold cross-validation runs, generalizing discrete hard-counting schemes into a continuous estimator that incorporates both frequency and magnitude of loss deviations. The central claim is that high SLA scores reliably flag ambiguous or mislabeled samples; experiments on a public fundus dataset are said to show consistent outperformance over hard-counting baselines at all noise levels together with substantially faster convergence, especially at low noise ratios.

Significance. If the core assumption holds, SLA would supply an interpretable, statistically stable, and training-procedure-agnostic noisiness score usable on any classification task without requiring task-specific models or extra annotations. This could streamline re-annotation pipelines for large medical imaging collections where inter-observer variability is common.

major comments (2)

[Method formulation and Experiments] The manuscript's central interpretive claim—that deviations in fold-level validation losses are caused primarily by label noise rather than by random weight initialization, optimizer stochasticity, batch ordering, or the particular train/validation partition—receives no isolating experiments or controls. This assumption is load-bearing for both the interpretability of SLA scores and the claimed superiority over hard-counting (see the method formulation and the experimental comparison sections).
[Abstract and Experiments] No quantitative metrics, error bars, or detailed experimental protocol (number of CV repetitions, exact standardization procedure, statistical tests) are supplied in support of the abstract's assertions of “consistent outperformance” and “substantially faster convergence.” Without these, the empirical claims cannot be verified or reproduced.

minor comments (2)

[Method] Notation for the standardization step and the aggregation operator should be introduced with explicit equations rather than prose descriptions only.
[Experimental setup] The paper should clarify whether the same model architecture and hyper-parameters are used for every CV run, as any variation would confound the loss-deviation signal.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and describe the revisions planned for the manuscript.

read point-by-point responses

Referee: [Method formulation and Experiments] The manuscript's central interpretive claim—that deviations in fold-level validation losses are caused primarily by label noise rather than by random weight initialization, optimizer stochasticity, batch ordering, or the particular train/validation partition—receives no isolating experiments or controls. This assumption is load-bearing for both the interpretability of SLA scores and the claimed superiority over hard-counting (see the method formulation and the experimental comparison sections).

Authors: We appreciate the referee's emphasis on the need to isolate label noise as the primary source of loss deviations. The SLA formulation uses repeated cross-validation and per-fold z-score standardization precisely to reduce the influence of stochastic training factors and partition effects, allowing the aggregated score to reflect consistent performance deviations. Nevertheless, we agree that explicit isolating controls would strengthen the interpretability claim. In the revised manuscript we will add a controlled experiment on a clean dataset variant, comparing SLA scores under synthetic label noise against runs that vary only initialization and batch order. We will also expand the discussion section to quantify the expected contribution of non-noise factors under the repeated-CV regime. revision: yes
Referee: [Abstract and Experiments] No quantitative metrics, error bars, or detailed experimental protocol (number of CV repetitions, exact standardization procedure, statistical tests) are supplied in support of the abstract's assertions of “consistent outperformance” and “substantially faster convergence.” Without these, the empirical claims cannot be verified or reproduced.

Authors: We acknowledge that the abstract would benefit from greater specificity to support its claims. The full experimental section already specifies the repeated k-fold protocol, the exact standardization (z-score per validation fold), the number of repetitions, and reports performance metrics with variability across runs. To improve accessibility and reproducibility, we will revise the abstract to include concise quantitative summaries (e.g., relative gains and convergence behavior) together with references to the error bars and statistical comparisons presented in the results. The methods section will be updated to foreground the full protocol and any significance testing. revision: yes

Circularity Check

0 steps flagged

No circularity: SLA is an empirical aggregation of observed CV losses with no self-referential definitions or fitted predictions.

full rationale

The SLA procedure standardizes and aggregates per-sample validation losses obtained from repeated k-fold cross-validation runs. This is a direct computation from empirical data and does not reduce any claimed noisiness score to a fitted parameter, self-definition, or self-citation chain. The paper presents SLA as a continuous generalization of hard-counting without invoking uniqueness theorems, ansatzes from prior self-work, or renaming of known results. The interpretability claim rests on an external assumption about the source of loss variation rather than on any definitional equivalence within the method itself. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that standardized validation-loss deviations primarily reflect label noise. No free parameters or invented entities are mentioned in the abstract.

axioms (1)

domain assumption Standardized fold-level validation losses indicate label reliability
Core premise of the SLA scoring procedure as stated in the abstract.

pith-pipeline@v0.9.0 · 5435 in / 1225 out tokens · 56163 ms · 2026-05-12T03:03:25.559556+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages · 1 internal anchor

[1]

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

INTRODUCTION Deep learning has achieved remarkable progress in computer vision and medical image analysis. However, its performance critically depends on large-scale datasets with reliable annotations . In clinical imaging, obtaining such annotations is both costly and challenging: expert labeling is time-consuming and often inconsistent due to inter- obs...

work page internal anchor Pith review Pith/arXiv arXiv 2026
[2]

METHOD 2.1. Overview The proposed framework, calledStandardized Loss Aggregation (SLA), estimates sample-level label noisiness by aggregating stan- dardized fold-level validation losses across repeated K-fold cross- validation. Unlike discrete count-based methods that only record occurrences in the worst-performing folds, SLA continuously inte- grates nor...

work page 2048
[3]

RESULTS 3.1. Controlled label-noise experiments We evaluated the proposed framework under controlled synthetic conditions using theJustRAIGSdataset [ 14], where random label flips were applied to simulate noise ratios ranging from 0.1% to 10%. Each experiment was conducted with K= 5 folds and R= 100,000 repetitions. Figure 2 summarizes the area under the ...

work page
[4]

DISCUSSION The proposedStandardized Loss Aggregation (SLA)framework pro- vides a statistically grounded and task-agnostic approach for detecting noisy labels at the sample level. By aggregating standardized fold- level validation losses across repeated cross-validation runs, SLA quantifies label reliability as the expected standardized deviation of each s...

work page
[5]

Ethical approval was not required as confirmed by the license attached with the open access data

COMPLIANCE WITH ETHICAL STANDARDS This research study was conducted retrospectively using human sub- ject data made available in open access. Ethical approval was not required as confirmed by the license attached with the open access data

work page
[6]

CONFLICT OF INTEREST The authors are employees of VUNO Inc., but declare that they have no competing financial or non-financial interests related to this work

work page
[7]

A sur- vey of label-noise deep learning for medical image analysis,

J. Shi, K. Zhang, C. Guo, Y . Yang, Y . Xu, and J. Wu, “A sur- vey of label-noise deep learning for medical image analysis,” Medical image analysis, vol. 95, p. 103166, 2024

work page 2024
[8]

Probabilis- tic modeling of inter-and intra-observer variability in medical image segmentation,

A. Schmidt, P. Morales-Alvarez, and R. Molina, “Probabilis- tic modeling of inter-and intra-observer variability in medical image segmentation,” inProceedings of the IEEE/CVF interna- tional conference on computer vision, 2023, pp. 21 097–21 106

work page 2023
[9]

The limits of fair medical imaging ai in real-world generaliza- tion,

Y . Yang, H. Zhang, J. W. Gichoya, D. Katabi, and M. Ghassemi, “The limits of fair medical imaging ai in real-world generaliza- tion,”Nature Medicine, vol. 30, no. 10, pp. 2838–2848, 2024

work page 2024
[10]

Learning from noisy labels with deep neural networks: A survey,

H. Song, M. Kim, D. Park, Y . Shin, and J.-G. Lee, “Learning from noisy labels with deep neural networks: A survey,”IEEE transactions on neural networks and learning systems, vol. 34, no. 11, pp. 8135–8153, 2022

work page 2022
[11]

Making deep neural networks robust to label noise: A loss correction approach,

G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu, “Making deep neural networks robust to label noise: A loss correction approach,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1944–1952

work page 2017
[12]

Robust loss functions under label noise for deep neural networks,

A. Ghosh, H. Kumar, and P. S. Sastry, “Robust loss functions under label noise for deep neural networks,” inProceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1, 2017

work page 2017
[13]

Sym- metric cross entropy for robust learning with noisy labels,

Y . Wang, X. Ma, Z. Chen, Y . Luo, J. Yi, and J. Bailey, “Sym- metric cross entropy for robust learning with noisy labels,” in Proceedings of the IEEE/CVF international conference on com- puter vision, 2019, pp. 322–330

work page 2019
[14]

Deep learning with noisy labels in medical prediction problems: a scoping review,

Y . Wei, Y . Deng, C. Sun, M. Lin, H. Jiang, and Y . Peng, “Deep learning with noisy labels in medical prediction problems: a scoping review,”Journal of the American Medical Informatics Association, vol. 31, no. 7, pp. 1596–1607, 2024

work page 2024
[15]

Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,

L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei, “Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,” inInternational conference on machine learning. PMLR, 2018, pp. 2304–2313

work page 2018
[16]

Co-teaching: Robust training of deep neural networks with extremely noisy labels,

B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,”Advances in neural information processing systems, vol. 31, 2018

work page 2018
[17]

O2u-net: A simple noisy label detection approach for deep neural networks,

J. Huang, L. Qu, R. Jia, and B. Zhao, “O2u-net: A simple noisy label detection approach for deep neural networks,” inProceed- ings of the IEEE/CVF international conference on computer vision, 2019, pp. 3326–3334

work page 2019
[18]

Fine samples for learning with noisy labels,

T. Kim, J. Ko, J. Choi, S.-Y . Yunet al., “Fine samples for learning with noisy labels,”Advances in Neural Information Processing Systems, vol. 34, pp. 24 137–24 149, 2021

work page 2021
[19]

Detect- ing noisy labels with repeated cross-validations,

J. Chen, V . Ramanathan, T. Xu, and A. L. Martel, “Detect- ing noisy labels with repeated cross-validations,” inInterna- tional Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2024, pp. 197–207

work page 2024
[20]

Justraigs: Justified referral in ai glaucoma screening challenge,

Y . Madadi, H. Raja, K. A. Vermeer, H. G. Lemij, X. Huang, E. Kim, S. Lee, G. Kwon, H. Kim, J. Kimet al., “Justraigs: Justified referral in ai glaucoma screening challenge,”IEEE Transactions on Medical Imaging, 2025

work page 2025
[21]

A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision,

J. Silva-Rodriguez, H. Chakor, R. Kobbi, J. Dolz, and I. B. Ayed, “A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision,”Medical Image Analysis, vol. 99, p. 103357, 2025

work page 2025
[22]

Confident learning: Estimating uncertainty in dataset labels,

C. Northcutt, L. Jiang, and I. Chuang, “Confident learning: Estimating uncertainty in dataset labels,”Journal of Artificial Intelligence Research, vol. 70, pp. 1373–1411, 2021

work page 2021
[23]

Ensemble methods in machine learning,

T. G. Dietterich, “Ensemble methods in machine learning,” in International workshop on multiple classifier systems. Springer, 2000, pp. 1–15

work page 2000
[24]

Dividemix: Learning with noisy labels as semi-supervised learning,

J. Li, R. Socher, and S. C. Hoi, “Dividemix: Learning with noisy labels as semi-supervised learning,” inICLR, 2020

work page 2020