CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis

Chengxuan Qian; Chongwen Lyu; Jun Chen; Kai Han; Lele Ma; Siqi Ma; Zhe Liu; Zheng Pang

REVIEW 4 major objections 3 minor 31 references

CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis

T0 review · 4 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that CLIMD, a curriculum-learning framework that orders multimodal samples by intra-modal confidence and inter-modal complementarity under a class-distribution-guided scheduler, outperforms state-of-the-art methods on…

desk verdict Plausible curriculum-learning framework for imbalanced multimodal diagnosis, but the only inspectable evidence is the abstract; the supplied full text is corrupted, so the empirical claims cannot be checked. read the letter →

arxiv 2508.01594 v1 pith:QIGR4LQC submitted 2025-08-03 cs.CV

Kai Han , Chongwen Lyu , Lele Ma , Chengxuan Qian , Siqi Ma , Zheng Pang , Jun Chen , Zhe Liu This is my paper · ORCID

classification cs.CV

keywords curriculumlearningmultimodalfusionclassimbalancemedicaldiagnosisintra-modalconfidenceinter-modalcomplementarityplug-and-playtrainingminority-class

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

The paper is trying to establish that class imbalance in multimodal medical diagnosis is best handled not by resampling rare samples or reweighting their losses, but by controlling the order in which the model learns them. It proposes a curriculum-learning framework, CLIMD, that scores each training sample by how uncertain the individual modalities are and by how much the modalities complement each other, then feeds samples in an order that gradually exposes the model to the true imbalanced class distribution. The authors argue this avoids the overfitting and underfitting that plague resampling and loss reweighting, and that it captures cross-modal interactions that those methods ignore. They report that on multiple multimodal medical datasets, CLIMD outperforms existing approaches across several metrics and can be plugged into other models without changing their architecture.

What carries the argument

The load-bearing machinery is the multimodal curriculum measurer paired with the class distribution-guided scheduler. The measurer assigns each sample a difficulty score from two indicators: intra-modal confidence, how certain each modality branch is about the sample, and inter-modal complementarity, how much the modalities add to each other's evidence. The scheduler uses that score to present samples from easy to hard while gradually exposing the model to the imbalanced class distribution, so that rare classes receive appropriately timed emphasis.

What would settle it

Train CLIMD and an otherwise identical baseline with fixed random sample order on the same imbalanced multimodal dataset, using the same epochs and seeds; if the curriculum does not improve minority-class recall over that baseline, the claimed benefit of difficulty ordering is not present.

Watch

Extended reading notes

Core claim

The central claim is a training-ordering rule: a model should learn imbalanced multimodal data by progressing from easy to hard samples, where hardness is determined by a combination of each modality's own confidence and the information the modalities contribute to one another. A class distribution-guided scheduler paces this progression so that minority classes are not abandoned early and not forced too aggressively. The paper reports that this ordering improves diagnostic performance on imbalanced multimodal medical datasets compared with state-of-the-art methods, and that the framework is plug-and-play, meaning existing multimodal models can adopt it without redesign.

Load-bearing premise

The whole approach rests on the assumption that a model's confidence in each modality and the agreement between modalities reliably say which samples are easier to learn; if those scores mislead early in training, the curriculum could train on the wrong samples and worsen the imbalance.

Editorial extensions

If this is right

Existing multimodal diagnostic models can adopt CLIMD without architectural changes, so the benefit is not tied to one particular fusion design.
Minority classes receive a training regime that avoids the overfitting of naive resampling and the underfitting of simple loss reweighting.
Cross-modal interactions are exploited during sample ordering, not only during fusion, so disagreement between modalities becomes a training signal.
The same ordering principle should transfer to other imbalanced multimodal tasks whenever per-modality confidence and complementarity can be computed.

Reading between the lines

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

The difficulty score could be reused as a signal for active learning or noisy-label detection, since the curriculum marks exactly the samples the model is least certain about.
Because the scheduler is separate from the loss function, CLIMD could potentially be combined with loss reweighting or focal loss rather than replacing them.
A clean isolation experiment would be CLIMD with the class-distribution scheduler removed, which would reveal whether gains come from sample ordering or from the pacing of class exposure.
One could test whether the same curriculum principle helps when a third, unreliable modality is added, since the complementarity term should make the model learn to discount it.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The short version: this is a plausible, well-scoped curriculum-learning framework for imbalanced multimodal diagnosis, combining intra-modal confidence, inter-modal complementarity, and a class-distribution-guided scheduler. The abstract is coherent and the public code link is a plus. But our copy of the full text is corrupted mojibake, so every empirical claim is unverifiable from what I have. The paper could be completely sound; I just can't check.

What is actually new: the specific integration of confidence-based and complementarity-based difficulty scoring into a curriculum for imbalanced multimodal medical data, presented as a plug-and-play framework. The individual ingredients are established, but the combination for this problem is not something I can point to as prior work. The abstract does not overreach in tone—it claims SOTA gains without numbers, which is normal for an abstract.

Soft spots, in proportion: the evidence problem dominates. No tables, ablations, baselines, or dataset names are inspectable in the supplied text. The stress-test concern about early-training confidence miscalibration is plausible but speculative: if the curriculum relies on the model's own confidence, minority samples with low confidence could be deferred, and the abstract mentions no calibration analysis or minority-stratified ablation to rule that out. That is a missing validation, not a demonstrated flaw. If the full paper includes those ablations, the concern disappears. I would also want the datasets named in the abstract or introduction, since 'multiple multimodal medical datasets' is too vague to assess scope.

Who this is for: researchers working on medical imaging AI with imbalanced multimodal data, and anyone interested in curriculum learning as a training strategy. A serious referee should get this paper because the problem is real, the method is plausible, and the code is promised—reviewers can actually run it.

My recommendation: send it to peer review. Put the confidence-calibration interaction on the reviewer checklist, and ask for a minority-stratified ablation of the curriculum. I won't cite it until I see the actual numbers, but it deserves a fair read.

Referee Report

4 major / 3 minor

Summary. The paper proposes CLIMD, a curriculum learning framework for imbalanced multimodal diagnosis. It combines two indicators in a curriculum measurer (intra-modal confidence and inter-modal complementarity) with a class-distribution-guided training scheduler, aiming to progressively adapt the model to imbalanced class distributions. The abstract claims state-of-the-art performance on multiple multimodal medical datasets, as well as plug-and-play integration into other models. However, the supplied full text is heavily corrupted mojibake, so the technical definitions, equations, experimental tables, and ablations could not be inspected; only the abstract and a few fragmentary English tokens are readable.

Significance. If substantiated, the paper addresses an important practical problem: class imbalance in multimodal medical diagnosis. The proposed mechanism of ordering samples by multimodal difficulty, rather than relying on resampling or loss reweighting, is a plausible and interesting direction, and the plug-and-play claim would make the framework broadly useful. The manuscript also provides a public code URL, which is a strength. However, the submitted version contains no readable experimental evidence: the abstract gives no quantitative results, and the full text is corrupted. The core mechanism of confidence/complementarity-based difficulty ordering is also unvalidated for minority classes, which is the population the method is designed to help. The significance is therefore conditional on a clean version providing the missing empirical support.

major comments (4)

[Abstract] The central empirical claim that CLIMD 'outperforms state-of-the-art approaches across various metrics' is asserted without any quantitative support: no dataset names, metrics, baseline identifiers, effect sizes, or variance estimates appear in the abstract. Because this is an empirical paper, the experimental evidence is load-bearing, and as submitted there is nothing to verify.
[Full text (corrupted rendering)] The full text is rendered as mojibake, so no equation, algorithm, table, or ablation can be examined. I could not locate readable definitions of the curriculum measurer, the inter-modal complementarity score, the class-distribution scheduler, or the training objective. This prevents verification of every central technical step and makes the manuscript unreviewable in its current form.
[Curriculum measurer (minority-class calibration)] The difficulty-ordering mechanism is a correctness risk for the very minority classes the method targets. Early in training, model confidence on imbalanced data is likely miscalibrated: minority-class samples may receive low confidence regardless of true difficulty, while majority easy samples receive high confidence. If CLIMD defers low-confidence minority samples until late, it could suppress minority-class feature learning. The manuscript provides no calibration analysis, no oracle comparison for the difficulty ordering, and no minority-stratified ablation. Since the curriculum measurer is the central novelty, this missing validation is load-bearing for the claimed imbalance improvements.
[Experimental evaluation] No readable evidence of experimental rigor is present: there are no visible error bars, number of independent runs, statistical tests, or per-class performance metrics. Even if the original tables contain such information, the submitted version does not allow me to determine whether the claimed state-of-the-art gains are statistically significant or consistent across datasets.

minor comments (3)

[Abstract] The phrase 'excels in handling imbalanced multimodal medical data' is vague; please specify the imbalance ratios used and the minority-class metrics reported (e.g., F1, recall, or AUC).
[Full text] The rendering of the manuscript is corrupted at the level of character encoding; please ensure the arXiv submission source is correctly encoded before resubmission.
[Abstract] The abbreviation 'CL' appears in the abstract without being expanded in the abstract body; consider writing 'curriculum learning' at first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found; the central claims are empirical and evaluated against external baselines, and no equation-level reduction to inputs is inspectable.

full rationale

The supplied text is a corrupted/mojibake rendering of the manuscript, so no equation-level derivation chain is inspectable. The abstract presents CLIMD as a curriculum learning framework combining intra-modal confidence, inter-modal complementarity, and a class distribution-guided scheduler, with claims supported by 'extensive experiments on multiple multimodal medical datasets' against state-of-the-art approaches. There is no indication that any predicted quantity is defined in terms of the claimed output, that a fitted parameter is renamed as a prediction, or that a load-bearing premise rests on a self-citation. The curriculum measurer's use of the model's own confidence is a standard training-loop self-reference in curriculum learning, not a circular derivation of the type this axis targets. Per the hard rules, circularity may be claimed only when the paper can be quoted and the specific reduction exhibited; because the full text is unreadable in the provided rendering and no such reduction appears in the abstract, the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

From the abstract, the only identifiable assumptions are domain-level: the clinical utility of multimodal data, the harm of class imbalance, and the reliability of confidence and complementarity as difficulty signals. No free parameters are stated in the abstract; the unseen full text would be needed to audit hyperparameters like curriculum temperature, scheduling rates, or loss weights.

assumptions (3)

domain assumption Class imbalance in multimodal medical data degrades minority-class learning.
Assumed as the motivating problem in the abstract; not proven in the abstract.
domain assumption Intra-modal confidence and inter-modal complementarity are valid and computable indicators of sample difficulty.
The curriculum measurer depends on these indicators; their definitions and reliability are not visible in the abstract.
domain assumption Curriculum ordering from easy to hard improves generalization under imbalance.
Standard curriculum learning assumption invoked by the method; not established in the abstract.

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Cite this review

Pith. "Pith review of CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis." pith.science (2026). https://pith.science/paper/QIGR4LQC

@misc{pith2026250801594,
  author       = {Pith},
  title        = {Pith review of: CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QIGR4LQC}},
  note         = {Machine review of arXiv:2508.01594}
}

read the original abstract

Clinicians usually combine information from multiple sources to achieve the most accurate diagnosis, and this has sparked increasing interest in leveraging multimodal deep learning for diagnosis. However, in real clinical scenarios, due to differences in incidence rates, multimodal medical data commonly face the issue of class imbalance, which makes it difficult to adequately learn the features of minority classes. Most existing methods tackle this issue with resampling or loss reweighting, but they are prone to overfitting or underfitting and fail to capture cross-modal interactions. Therefore, we propose a Curriculum Learning framework for Imbalanced Multimodal Diagnosis (CLIMD). Specifically, we first design multimodal curriculum measurer that combines two indicators, intra-modal confidence and inter-modal complementarity, to enable the model to focus on key samples and gradually adapt to complex category distributions. Additionally, a class distribution-guided training scheduler is introduced, which enables the model to progressively adapt to the imbalanced class distribution during training. Extensive experiments on multiple multimodal medical datasets demonstrate that the proposed method outperforms state-of-the-art approaches across various metrics and excels in handling imbalanced multimodal medical data. Furthermore, as a plug-and-play CL framework, CLIMD can be easily integrated into other models, offering a promising path for improving multimodal disease diagnosis accuracy. Code is publicly available at https://github.com/KHan-UJS/CLIMD.

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

In: Proceedings of the 26th annual international conference on machine learning

Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning. pp. 41--48 (2009)

work page 2009

[2] [2]

Journal of artificial intelligence research 16, 321--357 (2002)

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16, 321--357 (2002)

work page 2002

[3] [3]

Cognition 48(1), 71--99 (1993)

Elman, J.L.: Learning and development in neural networks: The importance of starting small. Cognition 48(1), 71--99 (1993)

work page 1993

[4] [4]

IEEE Transactions on Biomedical Engineering 68(6), 1913--1922 (2020)

Elola, A., Aramendi, E., Irusta, U., Berve, P.O., Wik, L.: Multimodal algorithms for the classification of circulation states during out-of-hospital cardiac arrest. IEEE Transactions on Biomedical Engineering 68(6), 1913--1922 (2020)

work page 2020

[5] [5]

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

Fang, Y., Wu, S., Zhang, S., Huang, C., Zeng, T., Xing, X., Walsh, S., Yang, G.: Dynamic multimodal information bottleneck for multimodality classification. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 7696--7706 (2024)

work page 2024

[6] [6]

Journal of Biomedical Informatics p

Ge, Y., Guo, Y., Das, S., Al-Garadi, M.A., Sarker, A.: Few-shot learning for medical text: A review of advances, trends, and opportunities. Journal of Biomedical Informatics p. 104458 (2023)

work page 2023

[7] [7]

IEEE Transactions on Medical Imaging (2025)

Han, K., Wang, S., Chen, J., Qian, C., Lyu, C., Ma, S., Qiu, C., Sheng, V.S., Huang, Q., Liu, Z.: Region uncertainty estimation for medical image segmentation with noisy labels. IEEE Transactions on Medical Imaging (2025)

2025

[8] [8]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Han, Z., Yang, F., Huang, J., Zhang, C., Yao, J.: Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 20707--20717 (2022)

work page 2022