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REVIEW 3 major objections 4 minor 60 references

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read CUFIT, a curriculum fine-tuning paradigm, uses a frozen linear probe to select clean samples for two adapters and reports consistent gains over prior noisy-label methods on four medical benchmarks.

desk verdict CUFIT is a sensible and likely-reproducible method with real gains, but the 60%-noise mechanism claim is overstated and the missing error bars and baseline details need referee attention. read the letter →

arxiv 2412.00150 v1 pith:2OKQSQND submitted 2024-11-29 cs.CV eess.IV

Yeonguk Yu , Minhwan Ko , Sungho Shin , Kangmin Kim , Kyoobin Lee This is my paper · ORCID
classification cs.CVeess.IV
keywords curriculumlearninglabelnoisevisionfoundationmodelsmedicalimageclassificationcleansampleselectionlinearprobingparameter-efficientfine-tuningadapters
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

Clean-sample selection methods for learning under label noise usually assume training starts from scratch, which wastes the rich features of pretrained vision foundation models. CUFIT instead uses a linear probe on a frozen foundation model as the first selector: because it never updates the feature extractor, it is less prone to memorizing wrong labels. The samples it flags as clean train an intermediate adapter, whose selections train a final adapter, and only this final adapter is used for prediction. The paper reports that this curriculum consistently beats prior selection methods, e.g., by 5.0%, 2.1%, 4.6%, and 5.8% at 40% symmetric noise on HAM10000, APTOS-2019, BloodMnist, and OrgancMnist. If correct, it makes pretrained models usable on noisy medical datasets without noise-rate hyperparameters or architectural loss changes.

What carries the argument

The load-bearing object is the three-module curriculum LPM→IAM→LAM with the agreement criterion as the selection rule: a training sample is treated as clean when the selecting module's argmax prediction equals the annotated label. The linear probing module never updates the frozen vision foundation model, so its selections stay stable under label noise; the intermediate adapter module adapts the features and selects a larger clean set; the last adapter module is trained on that set and used for final predictions. The chain needs no noise-rate hyperparameter and applies to any adapter inserted into a frozen transformer.

What would settle it

Compute the precision of the samples selected by the linear probing module on a synthetic 60% symmetric-noise version of any of the four medical benchmarks; if that selected set is not majority clean, meaning precision falls to 50% or below, the curriculum's input is corrupted and CUFIT's reported advantage would be expected to disappear.

Watch

Extended reading notes

Core claim

The central discovery is that the resistance of linear probing to label noise can be converted into a training curriculum for adapters. Given a frozen pretrained vision foundation model, CUFIT trains a linear classifier on all samples; this linear probing module (LPM) selects as clean any sample whose predicted class equals its annotation (the agreement criterion). Those samples train an intermediate adapter module (IAM), which in turn selects clean samples for the last adapter module (LAM). Because the LPM improves precision and the IAM improves recall relative to it, the curriculum expands the clean set available to the final classifier. In the paper's experiments on four simulated noisy medical benchmarks and one real-world noisy retina benchmark, this scheme outperforms prior sample-selection baselines and stays effective at 60% and even 80% noise.

Load-bearing premise

The load-bearing premise is that a sample whose label agrees with the current module's prediction is genuinely clean; this requires the true label to be the module's most common prediction within each annotated class, which is not guaranteed at the 60% symmetric-noise rate tested.

Editorial extensions

If this is right

  • At 40% symmetric noise, CUFIT improves over the next-best baselines by 5.0, 2.1, 4.6, and 5.8 accuracy points on HAM10000, APTOS-2019, BloodMnist, and OrgancMnist.
  • Gains over the strongest sample-selection baseline widen as noise rises, from about 0.7 point at 10% noise to 4.0 points at 60% noise on HAM10000.
  • On the real-world Kaggle-EyePACS noisy retina training set, CUFIT achieves the highest combined accuracy on the APTOS-2019 and FGADR test sets, 64.4% versus 63.9% for the next method.
  • The same curriculum lifts accuracy under 80% symmetric noise on CIFAR-10 and CIFAR-100 and on the real-world ANIMAL10N benchmark, so the mechanism is not restricted to medical images.
  • Because CUFIT works across several vision foundation models and several adapter types, the curriculum itself, not a particular adapter, is the source of the reported gain.

Reading between the lines

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

  • The agreement criterion's real requirement is that the true label be the linear probe's modal prediction within each annotated class; under symmetric noise above 50% this is stricter than the paper's majority-clean phrasing and is directly measurable, so the method's failure boundary could be mapped empirically.
  • Because the selection modules and the adapter can be swapped independently, CUFIT-like curricula could be applied to other parameter-efficient fine-tuning methods or to any frozen backbone in domains far from medical imaging; nothing in the design is medical-specific.
  • One could decouple the curriculum from the hard argmax match by using the linear probe's confidence as a threshold, tuning the precision-recall balance of the selected clean set and potentially pushing the method past the 60% noise regime reported.
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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

3 major / 4 minor

Summary. The paper proposes CUFIT, a curriculum fine-tuning paradigm for vision foundation models under label noise. A linear probing module is trained on all samples; an intermediate adapter module is trained only on samples whose linear-probe argmax agrees with the annotation; a final adapter module is trained on samples selected by the intermediate adapter. At inference, only the last adapter is used. Experiments on four medical datasets with symmetric noise rates of 10-60%, on a real-world noisy retinopathy benchmark, and on natural image benchmarks compare against full fine-tuning, linear probing, Rein, Co-teaching, JoCor, and CoDis. Table 1 reports consistent gains, e.g., 5.0%, 2.1%, 4.6%, and 5.8% at 40% noise on HAM10000, APTOS-2019, BloodMnist, and OrgancMnist, and Section 6 analyzes label precision/recall, adapter/backbone variations, and CNN baselines.

Significance. If the empirical claims are supported, the contribution is practically valuable: it shows that frozen VFM features can drive clean-sample selection without a noise-rate hyperparameter, and the curriculum ordering (linear probe then adapters) is simple and general. Strengths include public code, evaluation on external test sets, no fitted constants in the selection criterion, and process-level diagnostics in Figure 3 that show higher label precision and recall than CoDis at 40% noise. The main gaps are the missing empirical support for the 60% noise claims and the absence of any variance reporting; both are addressable by additional experiments. I find no circularity in the evaluation, since the reported accuracies are measured on held-out test sets.

major comments (3)
  1. [Sec. 4.2, Eqs. (6)-(7); Table 1 at noise rate 0.6] The justification for the agreement criterion is that clean labels are in the majority within a noisy class. Under the symmetric noise model used in Sec. 5.1, at r=0.6 each observed class contains, in expectation, 40% clean and 60% noisy samples, so the stated premise is false. The selection rule could still work if the classifier's argmax is a better predictor of the true label than the observed annotation, i.e., if the true class is the modal prediction because wrong labels are spread over k-1 classes, but this condition is neither stated in Sec. 4.2 nor verified empirically. Figure 3 reports label precision/recall only at 40% noise, and Table 1 reports only final test accuracy; the 60% rows, including the large BloodMnist margin (98.2 vs 88.7), are therefore not backed by the mechanism described. Please add selected-set precision/recall at 60% noise (or an equivalent diagnostic) and either prove or empirically test the modal-prediction condition.
  2. [Tables 1-4] No standard deviations, confidence intervals, or number of seeds are reported; the values are stated only as test accuracy averaged over the last ten epochs. Without across-seed variability, the abstract's specific margins (e.g., 5.0%, 2.1%, 4.6%, and 5.8% at 40% noise) cannot be distinguished from run-to-run variation. Because the paper's central claim is comparative, report mean and standard deviation over at least three seeds for the main tables, or otherwise justify the stability of the margins.
  3. [Sec. 5.1, Baselines] Co-teaching, JoCor, and CoDis are two-network methods, but the manuscript does not specify how these baselines are instantiated with DINOv2 and the Rein adapter. It is unclear whether each of the two networks has its own frozen VFM backbone and adapter, whether they share the frozen backbone, and how the noise-rate hyperparameter required by these methods is set for the simulated benchmarks (and for the real-world benchmark via the estimation method of [56]). Without these details, the fairness of the comparison cannot be assessed. Please document the exact two-network setup and hyperparameter choices, and ideally release the baseline code.
minor comments (4)
  1. [Sec. 5.2] The sentence claiming CUFIT achieves '0.85% relateively higher accuracy' than CoDis at 10% noise and '3.7%' at 60% noise on HAM10000 does not match Table 1 (82.6 vs 81.9 is 0.7 percentage points; 70.1 vs 66.1 is 4.0 percentage points). Correct the numbers or state explicitly that they are relative improvements.
  2. [Table 3] The header row is malformed, with entries like 'DINOv2ResNet' that make it hard to distinguish ResNet and DINOv2 columns. Also, the HAM10000 full-training DINOv2 value at 0.2 noise is 66.5 here but 62.6 in Table 1; the discrepancy should be resolved.
  3. [Sec. 6.2] The paragraph says 'we utilize four backbones' and lists BioMedCLIP and LoRA, while the preceding sentence and the Figure 4 caption describe three VFMs and three adapters. Please align the text with the figure.
  4. [Sec. 4] The text says that the modules are trained simultaneously on the current batch, while Figure 2 and the notation LPM→IAM→LAM suggest strictly sequential training; please clarify the temporal relationship.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: CUFIT's selection rule is self-referential only in the standard sample-selection sense, and every reported performance claim is anchored to external test sets and independent noise ground truth; the 60% majority-assumption flaw is an empirical concern, not a circular reduction.

full rationale

I traced the derivation chain of CUFIT. The only load-bearing selection mechanism is the agreement criterion in Eqs. (6)-(7), where a sample is selected as clean if the current module's argmax equals its annotation. This is self-referential in the usual sample-selection sense—the model selects its own training data—but the paper does not define any reported success metric as this agreement. The headline results in Tables 1, 2, and 4 are held-out test accuracies on external benchmark splits, and Figure 3's label precision/recall are computed against known synthetic noise, so the reported quantities are not equal to the selection rule by construction. No fitted hyperparameter is renamed as a prediction; the method explicitly avoids noise-rate estimation. The paper cites prior work for VFMs, adapters, and co-teaching, but there are no self-citations, and no cited result is used as the sole justification for the central empirical claim. The one substantive weakness, flagged in Section 4.2, is that the stated premise 'clean labels are in the majority within a noisy class' is arithmetically false for the 60% symmetric noise setting tested in Section 5.1; however, this is an empirical support problem (the selection mechanism may still work via modal prediction, but the paper does not measure selected-set precision at 60%), not a circular derivation. The 60% rows in Table 1 are external test numbers that could be disconfirmed without the method becoming tautological. Section 5.1 also explicitly scopes out loss-modifying SOTA methods, which limits the comparison but does not introduce circularity. Therefore no circular step can be exhibited, and the correct finding is no significant circularity.

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

CUFIT introduces no free scalar parameters or invented entities; its trainable parameters are network weights. The central claim rests on domain assumptions about clean-sample identifiability and frozen-feature robustness, both stated in the paper and only empirically motivated.

assumptions (4)
  • domain assumption Clean labels are in the majority within a noisy class, so agreement between prediction and annotation identifies clean samples (Sec 4.2).
    Underlies Eqs. 6 and 7. This statement is false for symmetric noise rates above 50% (e.g., the 60% rate tested in Sec 5.1), so the operative assumption must be weaker, e.g., the clean class is the modal prediction.
  • domain assumption Linear probing of a frozen VFM is robust to noisy labels because the feature extractor is not updated (Sec 1, Fig. 1).
    The foundation of CUFIT. Supported only empirically by Fig. 1 on HAM10000; it is not derived and may not hold when the VFM's features are weak for a target domain.
  • domain assumption Clean samples are easier to classify and are memorized before noisy ones, so small-loss/agreement selection generalizes (Sec 2, inherited from Co-teaching literature).
    CUFIT inherits this standard noisy-label assumption without re-validating it for VFM features.
  • domain assumption The selected benchmark noise (symmetric 10-60%) approximates real clinical label noise.
    Used to claim broad effectiveness; the real-world EyePACS experiment provides only partial external validation.

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Cite this review

Pith. "Pith review of Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise." pith.science (2026). https://pith.science/paper/2OKQSQND

@misc{pith2026241200150,
  author       = {Pith},
  title        = {Pith review of: Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OKQSQND}},
  note         = {Machine review of arXiv:2412.00150}
}
read the original abstract

Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a critical issue in medical datasets and can significantly degrade model performance. Previous clean sample selection methods have not utilized the well pre-trained features of vision foundation models (VFMs) and assumed that training begins from scratch. In this paper, we propose CUFIT, a curriculum fine-tuning paradigm of VFMs for medical image classification under label noise. Our method is motivated by the fact that linear probing of VFMs is relatively unaffected by noisy samples, as it does not update the feature extractor of the VFM, thus robustly classifying the training samples. Subsequently, curriculum fine-tuning of two adapters is conducted, starting with clean sample selection from the linear probing phase. Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks. Specifically, our method surpasses previous baselines by 5.0%, 2.1%, 4.6%, and 5.8% at a 40% noise rate on the HAM10000, APTOS-2019, BloodMnist, and OrgancMnist datasets, respectively. Furthermore, we provide extensive analyses to demonstrate the impact of our method on noisy label detection. For instance, our method shows higher label precision and recall compared to previous approaches. Our work highlights the potential of leveraging VFMs in medical image classification under challenging conditions of noisy labels.

Figures

Figures reproduced from arXiv: 2412.00150 by the authors.

Figure 1
Figure 1. Illustration of linear probing (a) and adapter usage (b). Specifically, the weights of the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of our proposed training framework, CUFIT, which consists of a pre-trained [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Illustration of label precision (a,d), label recall (b,e), and test accuracy (c,f) vs. epoch. The [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Test accuracy of our method with various VFMs (DINOv1 [ [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Pith tools

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