Pith. sign in

REVIEW 4 major objections 4 minor 38 references

Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A 30,047-parameter CNN retrained on its own misclassifications reports higher accuracy than ResNet-50 on two medical image benchmarks, using fewer training samples.

desk verdict IMET is a plausible hybrid of equal-class and misclassification-weighted sampling, but the reported accuracy gains over ResNet baselines are within sampling noise and lack the matched random-sampling baseline needed to support them. read the letter →

arxiv 2507.02979 v1 pith:Z3DJTKCZ submitted 2025-07-01 cs.CV cs.LG

Ruhaan Singh , Sreelekha Guggilam This is my paper · ORCID
classification cs.CVcs.LG
keywords IterativeMisclassificationErrorTrainingIMETcurriculumlearningcoresetselectionmedicalimageclassificationOCTMNISTPneumoniaMNISTclassimbalance
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 proposes Iterative Misclassification Error Training (IMET), a way of deciding which training images to show a CNN on each round. After an initial training pass, IMET evaluates the model on a balanced subsample of the smallest class, counts the misclassified images per class, and builds the next training set from two halves: equal numbers of samples per class, plus samples drawn from misclassified pools in proportion to each class's error rate. The paper's claim is that this hybrid selection rule lets a small 30,047-parameter CNN outperform the ResNet-50 baseline on two MedMNIST benchmarks while using fewer training images, with reported accuracies of 80.3% versus 77.6% on OCTMNIST and 90.2% versus 88.6% on PneumoniaMNIST. The reason this would matter is that medical datasets are often small, imbalanced, and noisy, so a training rule that gets more accuracy from less data could make diagnostic models more practical.

What carries the argument

The load-bearing object is the IMET data-update loop (Algorithm 3). It uses a balanced sub-sample $d_{sub}$ of training data, sized by the smallest class, to measure per-class misclassification counts; converts those counts into percentage weights $p_k$; and for each class draws $p_k\%$ of samples from the misclassified pool and $m$ equal samples from the class, so the next epoch's training set is a 50/50 mixture of error-focused and class-balanced data. The 9-layer CNN with 30,047 parameters is the testbed, but the mechanism that carries the claimed gain is this iterative resampling rule, not the architecture.

What would settle it

Run IMET and a random-sampling control with the same 9-layer CNN, optimizer, epochs, retraining rounds, and per-round training budget on OCTMNIST and PneumoniaMNIST. If the random control reaches or exceeds IMET's reported 80.3% and 90.2% accuracies, the claimed benefit of misclassification-weighted selection is not real.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that repeatedly retraining on the mistakes the model still makes, balanced against a fixed floor of equal class sampling, produces a more accurate classifier than standard full-data training with much larger networks. IMET keeps a balanced evaluation subset whose size is set by the smallest class, computes per-class misclassification rates on that subset, then forms each new training set as a 50/50 blend: half equal samples per class and half samples drawn from the misclassified images with probability proportional to class error. This is intended to keep attention on rare and edge-case classes without the oscillation that pure error-proportional weighting can cause. The paper reports that this procedure outperforms both equal-class and weighted sampling on OCTMNIST and PneumoniaMNIST, and matches or exceeds the accuracy and AUC of the ResNet-18 and ResNet-50 baselines with roughly 366-765 times fewer parameters and with thousands fewer training samples.

Load-bearing premise

The claim assumes the accuracy gain comes from IMET's selection rule, but the experiments do not fix how many retraining rounds are run, how much training happens per round, or how the final model is chosen, and they do not compare against randomly chosen training samples of the same size.

Editorial extensions

If this is right

  • A training rule that needs fewer images could lower the data and compute barrier for medical imaging models, where annotated data is scarce and expensive.
  • Small models trained with IMET could replace large pretrained backbones on MedMNIST-style tasks, making deployment on resource-constrained hardware more feasible.
  • Since IMET weights classes by their current misclassification rate, it should shift training toward rare and edge-case classes on imbalanced datasets.
  • The authors state the technique is not domain-specific and could be tested on transformers and large language models as a data-efficiency strategy.

Reading between the lines

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

  • A direct test that would sharpen the claim is to compare IMET against random sampling with the same per-round sample size and training budget; without that control, the tables do not by themselves show that the selection rule, rather than the sample count, drives the accuracy gain.
  • The 50/50 blend invites a sweep of the mixture ratio; the optimal balance may lie elsewhere, and the paper gives no evidence that 50 percent is the right operating point.
  • Because IMET's evaluation subset is capped by the smallest class, scaling to large, highly imbalanced datasets may require an alternative error estimate; a validation-set-based or per-class accuracy estimate could be tested as a substitute.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript proposes Iterative Misclassification Error Training (IMET), a training procedure that alternates equal-class sampling with misclassification-weighted sampling, and evaluates it on OCTMNIST and PneumoniaMNIST using a small 30,047-parameter CNN. The paper reports that IMET reaches 80.3% and 90.2% accuracy on the two test sets, compares these numbers with MedMNIST ResNet baselines, and concludes that IMET outperforms the benchmarks while using fewer training samples. The algorithmic idea is clearly described, but the experimental evidence as presented does not establish the central claim.

Significance. IMET is a plausible data-selection heuristic that could be useful for small, imbalanced medical image datasets if its reported advantage is real. The manuscript includes comparisons with two in-house sampling baselines and with published ResNet results, and it reports several metrics (accuracy, precision, recall, F1, AUC) on standardized MedMNIST benchmarks. However, the evaluation consists of single-run point estimates without uncertainty quantification, lacks a matched random-sampling control, and leaves key algorithm parameters unspecified. The strength of the claim that IMET 'outperforms' the ResNet benchmarks is therefore out of proportion to the evidence presented.

major comments (4)
  1. [Section IV-D, Tables 2-3] The claimed advantage over the ResNet baselines is within sampling noise. For OCTMNIST, 80.3% versus 77.6% on the 1,000-image test set is a difference of 27 correct images; the approximate standard error of the difference is about 1.8 percentage points, so the 95% confidence interval includes zero. For PneumoniaMNIST, 90.2% versus 88.6% on the 624-image test set is a 1.6-percentage-point difference with an approximate standard error of about 1.8 points. The paper reports no seeds, no repeated runs, no error bars, and no significance tests. Without these, the conclusion in Section V that IMET 'successfully outperform[ed]' the OCTMNIST and PneumoniaMNIST benchmark models is not supported.
  2. [Tables 2-3, Section IV-A] No control trains the same 30,047-parameter CNN on a random subsample matched to IMET's training-set size and class composition. The in-house baselines (Equal Class Sampling and Weighted Sampling) differ from IMET in both the selection rule and the sample count (31,016, 63,000, and 87,000 samples for OCTMNIST; 2,428, 4,700, and 2,800 for PneumoniaMNIST), so the higher IMET accuracy could be explained by sample size or class balancing rather than by misclassification-guided selection. A random-subsample baseline matched to IMET's final training distribution is needed to isolate the effect of the IMET selection mechanism.
  3. [Algorithm 3, Section III-A] The algorithm leaves Nrep, k, and the final-model selection rule unspecified, so the experiment is not reproducible and the reported sample counts are ambiguous. For OCTMNIST, where the smallest class in the training split is roughly m≈7,900, one round of Algorithm 3 contains at most about 5m≈39,500 samples (4m from the equal-class part plus at most m from the misclassified-weighted part), so the reported 87,000 samples in Table 2 cannot be a single-round training set. If it is a cumulative count across rounds, then '87,000 samples' counts repeated presentations of the same images and is not comparable with the ResNet's 97,477 single-pass training samples used in the Section V comparison. The authors should report Nrep, k, the per-round training-set sizes, the stopping criterion, and how the final model was selected; if the final round was chosen by test-set accuracy, that selection would be test-set leakage.
  4. [Table 3, Section V] The blanket statement that IMET outperforms the PneumoniaMNIST benchmark models is contradicted by the AUC column of Table 3: IMET has AUC 0.895, while all four ResNet baselines have AUC between 0.944 and 0.962. Even if the accuracy point estimate is higher, the conclusion that IMET 'successfully outperform[ed]' the benchmarks is not justified when a threshold-independent metric is substantially worse. The authors should either temper the claim or explain why accuracy should be privileged over AUC in this comparison.
minor comments (4)
  1. [Abstract, Section IV-C] The abstract contains 'senarious' (should be 'scenarios'), and Section IV-C uses 'Receiving Operator Curve' (should be 'Receiver Operating Characteristic').
  2. [Algorithm 2, Algorithm 3] The input line of Algorithm 2 and Algorithm 3 uses 'inital' (should be 'initial'), and Algorithm 3 line 10 has 'fromdk' with a missing space, making the instruction harder to parse.
  3. [Figure 4 caption] The caption states 'From right to left: CNV, DME, Drusen, and normal retina'; please confirm that the direction matches the order of the images in the figure.
  4. [Tables 2-3] The 'Samples' column should explicitly state whether the number refers to unique training samples, cumulative presentations across rounds, or the final round's training-set size; the current ambiguity directly affects the data-efficiency claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; IMET is an empirical training heuristic evaluated on held-out test sets.

full rationale

The paper is an empirical evaluation of a training heuristic, not a derivation, so there is no equation-level chain that could collapse into its own inputs. Algorithm 3 uses a fixed subset dsub of the training split only to identify misclassified samples and to construct subsequent training batches; the reported accuracies (80.3% on OCTMNIST, 90.2% on PneumoniaMNIST) are computed on the held-out MedMNIST test splits (1,000 and 624 images, respectively), which are never used for sample selection. Retraining on samples that were misclassified on dsub can improve training-set accuracy by construction, but the paper does not present dsub accuracy as its result, so no fitted parameter or selection signal is renamed as a held-out prediction. The ResNet baseline numbers are taken from the external MedMNIST benchmark, and the paper invokes no self-citation as load-bearing evidence; the authors do not cite their own prior work at all. The paper's own limitation statement acknowledges that the technique has only been tested on medical images, which is an honest scope caveat rather than a circularity. The concerns that do exist, such as missing error bars, the lack of a matched random-sampling control at the same sample size, and the unclear meaning of the cumulative '87,000 samples' figure, are statistical and evidential weaknesses, not instances of a result being equivalent to its input by definition or by self-citation. Therefore no circular step is identified, and the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the choice of the 50/50 mixing ratio and on unstated training hyperparameters. The method introduces no new physical or mathematical entities.

free parameters (3)
  • 50/50 mix ratio = 0.5
    Hand-chosen proportion of equal-class samples blended with misclassified samples in each IMET round; not ablated or justified.
  • Number of retraining rounds (Nrep) = not reported
    The loop in Algorithm 3 repeats 'Nrep' times, but no value is given anywhere in the paper; this affects the sample counts and compute budget.
  • Initial sample size (k) = not reported
    Algorithm 3 starts by sampling k points per class, but k is not specified.
assumptions (4)
  • domain assumption Misclassified samples are the most informative training examples
    Core premise of IMET; accepted from prior work on hard example mining, but not formally justified.
  • domain assumption The fixed evaluation subset dsub remains representative across retraining rounds
    Algorithm 3 computes errors on a single dsub drawn once; the paper does not discuss staleness as the model changes.
  • domain assumption MedMNIST benchmark numbers are reliable
    The comparison rests on published ResNet results from the MedMNIST collection, not reproduced here.
  • standard math Standard neural network training assumptions (optimizer, backpropagation)
    The paper relies on SGD/Adam and backprop as standard; no issue.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification." pith.science (2026). https://pith.science/paper/Z3DJTKCZ

@misc{pith2026250702979,
  author       = {Pith},
  title        = {Pith review of: Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z3DJTKCZ}},
  note         = {Machine review of arXiv:2507.02979}
}
read the original abstract

Deep learning models have proven to be effective on medical datasets for accurate diagnostic predictions from images. However, medical datasets often contain noisy, mislabeled, or poorly generalizable images, particularly for edge cases and anomalous outcomes. Additionally, high quality datasets are often small in sample size that can result in overfitting, where models memorize noise rather than learn generalizable patterns. This in particular, could pose serious risks in medical diagnostics where the risk associated with mis-classification can impact human life. Several data-efficient training strategies have emerged to address these constraints. In particular, coreset selection identifies compact subsets of the most representative samples, enabling training that approximates full-dataset performance while reducing computational overhead. On the other hand, curriculum learning relies on gradually increasing training difficulty and accelerating convergence. However, developing a generalizable difficulty ranking mechanism that works across diverse domains, datasets, and models while reducing the computational tasks and remains challenging. In this paper, we introduce Iterative Misclassification Error Training (IMET), a novel framework inspired by curriculum learning and coreset selection. The IMET approach is aimed to identify misclassified samples in order to streamline the training process, while prioritizing the model's attention to edge case senarious and rare outcomes. The paper evaluates IMET's performance on benchmark medical image classification datasets against state-of-the-art ResNet architectures. The results demonstrating IMET's potential for enhancing model robustness and accuracy in medical image analysis are also presented in the paper.

Figures

Figures reproduced from arXiv: 2507.02979 by the authors.

Figure 1
Figure 1. FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]
Figure 7
Figure 7. Figure 7: The highest AUC score was obtained by IMET with [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: FIGURE 7 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: FIGURE 9 [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 35 canonical work pages

  1. [1]

    D., Lou, Y ., Erginay, A., Clarida, W., Amelon, R., Folk, J

    Abràmoff, M. D., Lou, Y ., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., and Niemeijer, M. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Inves- tigative ophthalmology & visual science 57 , 13 (2016), 5200–5206

    work page 2016

  2. [2]

    Covid-19 prediction and detection using deep learning

    Alazab, M., Awajan, A., Mesleh, A., and Alhyari, S. Covid-19 prediction and detection using deep learning. International Journal of Computer Information Systems and Industrial Management Applications 12 (2020), 14–14

    work page 2020

  3. [3]

    Curriculum learning

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

    work page 2009

  4. [4]

    Effective diagnosis of alzheimer’s disease via multimodal fusion analysis framework

    Bi, X.-a., Cai, R., Wang, Y ., and Liu, Y . Effective diagnosis of alzheimer’s disease via multimodal fusion analysis framework. Frontiers in genetics 10 (2019), 976

    work page 2019

  5. [5]

    Efficient coreset selection with cluster-based methods

    Chai, C., Wang, J., Tang, N., Y uan, Y ., Liu, J., Deng, Y ., and Wang, G. Efficient coreset selection with cluster-based methods. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 167–178

    work page 2023

  6. [6]

    C., Giusti, A., Gambardella, L

    Cireşan, D. C., Giusti, A., Gambardella, L. M., and Schmidhuber, J. Mitosis detection in breast cancer histology images with deep neural networks. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II 16 (2013), Springer, pp. 411–418

    work page 2013

  7. [7]

    Automatic detection of invasive ductal carcinoma in whole slide images with con- volutional neural networks

    Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., and Madabhushi, A. Automatic detection of invasive ductal carcinoma in whole slide images with con- volutional neural networks. In Medical imaging 2014: Digital pathology (2014), vol. 9041, SPIE, p. 904103

    work page 2014

  8. [8]

    Core-sets: Updated survey

    Feldman, D. Core-sets: Updated survey. Sampling techniques for super- vised or unsupervised tasks (2020), 23–44

    work page 2020

Show all 38 references
  1. [9]

    R., and Huang, D

    Guo, S., Huang, W., Zhang, H., Zhuang, C., Dong, D., Scott, M. R., and Huang, D. Curriculumnet: Weakly supervised learning from large-scale web images. In Proceedings of the European conference on computer vision (ECCV) (2018), pp. 135–150

    2018

  2. [10]

    On the power of curriculum learning in training deep networks

    Hacohen, G., and Weinshall, D. On the power of curriculum learning in training deep networks. In International conference on machine learning (2019), PMLR, pp. 2535–2544

    2019

  3. [11]

    Borderline-smote: a new over- sampling method in imbalanced data sets learning

    Han, H., Wang, W.-Y ., and Mao, B.-H. Borderline-smote: a new over- sampling method in imbalanced data sets learning. In International conference on intelligent computing (2005), Springer, pp. 878–887

    2005

  4. [12]

    Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of gleason grade group from mri

    Hu, J., Shen, A., Qiao, X., Zhou, Z., Qian, X., Zheng, Y ., Bao, J., Wang, X., and Dai, Y . Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of gleason grade group from mri. Medical Physics 50, 4 (2023), 2279–2289

    2023

  5. [13]

    Jiang, L., Meng, D., Mitamura, T., and Hauptmann, A. G. Easy samples first: Self-paced reranking for zero-example multimedia search. In Pro- ceedings of the 22nd ACM international conference on Multimedia (2014), pp. 547–556

    2014

  6. [14]

    Not all samples are created equal: Deep learning with importance sampling

    Katharopoulos, A., and Fleuret, F. Not all samples are created equal: Deep learning with importance sampling. In International conference on machine learning (2018), PMLR, pp. 2525–2534. 10 VOLUME 11, 2023 TABLE 2. OCTMNIST Results Model Parameters Samples Accuracy AUC Precisi...

    2018

  7. [15]

    S., Goldbaum, M., Cai, W., V alentim, C

    Kermany, D. S., Goldbaum, M., Cai, W., V alentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Y ang, G., Wu, X., Y an, F., et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. cell 172, 5 (2018), 1122–1131

    2018

  8. [16]

    Retrieve: Coreset selection for efficient and robust semi-supervised learning

    Killamsetty, K., Zhao, X., Chen, F., and Iyer, R. Retrieve: Coreset selection for efficient and robust semi-supervised learning. Advances in neural information processing systems 34 (2021), 14488–14501

    2021

  9. [17]

    Kropp, M., Golubnitschaja, O., Mazurakova, A., Koklesova, L., Sargheini, N., V o, T.-T. K. S., de Clerck, E., Polivka Jr, J., Potuznik, P ., Polivka, J., et al. Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications—risks and miti...

    2023

  10. [18]

    Are all train- ing examples equally valuable? arXiv preprint arXiv:1311.6510 (2013)

    Lapedriza, A., Pirsiavash, H., Bylinskii, Z., and Torralba, A. Are all train- ing examples equally valuable? arXiv preprint arXiv:1311.6510 (2013)

    2013 arXiv

  11. [19]

    A survey of convolutional neural networks: analysis, applications, and prospects

    Li, Z., Liu, F., Y ang, W., Peng, S., and Zhou, J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems 33 , 12 (2021), 6999–7019

    2021

  12. [20]

    Early diagnosis of alzheimer’s disease with deep learning

    Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. Early diagnosis of alzheimer’s disease with deep learning. In 2014 IEEE 11th international symposium on biomedical imaging (ISBI) (2014), IEEE, pp. 1015–1018

    2014

  13. [21]

    B., Shanbhag, A

    Moser, B. B., Shanbhag, A. S., Frolov, S., Raue, F., Folz, J., and Dengel, A. A coreset selection of coreset selection literature: Introduction and recent advances. arXiv preprint arXiv:2505.17799 (2025)

    2025

  14. [22]

    K., Takkar, B., Chawla, R., and Kumar, A

    Padhy, S. K., Takkar, B., Chawla, R., and Kumar, A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian journal of ophthalmology 67, 7 (2019), 1004–1009

    2019

  15. [23]

    M., Harding, S

    Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P ., and Zheng, Y . Convolutional neural networks for diabetic retinopathy.Procedia computer science 90 (2016), 200–205

    2016

  16. [24]

    Machine learning from imbalanced data sets 101

    Provost, F. Machine learning from imbalanced data sets 101. In Proceed- ings of the AAAI’2000 workshop on imbalanced data sets (2000), vol. 68, AAAI Press, pp. 1–3

    2000

  17. [25]

    Imagenet large scale visual recognition challenge

    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. Imagenet large scale visual recognition challenge. International journal of computer vision 115 (2015), 211–252

    2015

  18. [26]

    Santiago, C., Barata, C., Sasdelli, M., Carneiro, G., and Nascimento, J. C. Low: Training deep neural networks by learning optimal sample weights. Pattern recognition 110 (2021), 107585

    2021

  19. [27]

    T., Rota, P ., and Sebe, N

    Soviany, P ., Ionescu, R. T., Rota, P ., and Sebe, N. Curriculum learning: A survey. International Journal of Computer Vision 130 , 6 (2022), 1526– 1565

    2022

  20. [28]

    Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome

    Sun, D., Li, A., Tang, B., and Wang, M. Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Computer methods and programs in biomedicine 161 (2018), 45–53

    2018

  21. [29]

    R., and Wang, M

    V enugopalan, J., Tong, L., Hassanzadeh, H. R., and Wang, M. D. Mul- timodal deep learning models for early detection of alzheimer’s disease stage. Scientific reports 11, 1 (2021), 3254

    2021

  22. [30]

    Wang, S., and Summers, R. M. Machine learning and radiology. Medical image analysis 16, 5 (2012), 933–951

    2012

  23. [31]

    A survey on curriculum learning

    Wang, X., Chen, Y ., and Zhu, W. A survey on curriculum learning. IEEE transactions on pattern analysis and machine intelligence 44 , 9 (2021), 4555–4576

    2021

  24. [32]

    Submodularity in data subset selection and active learning

    Wei, K., Iyer, R., and Bilmes, J. Submodularity in data subset selection and active learning. In International conference on machine learning (2015), PMLR, pp. 1954–1963

    2015

  25. [33]

    N., Y ang, Y ., Brankov, J

    Wernick, M. N., Y ang, Y ., Brankov, J. G., Y ourganov, G., and Strother, S. C. Machine learning in medical imaging. IEEE signal processing magazine 27, 4 (2010), 25–38

    2010

  26. [34]

    Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

    Xia, X., Liu, J., Y u, J., Shen, X., Han, B., and Liu, T. Moderate coreset: A universal method of data selection for real-world data-efficient deep learning. In The Eleventh International Conference on Learning Repre- sentations (2022)

    2022

  27. [35]

    Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

    Y ang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., and Ni, B. Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data 10, 1 (2023), 41

    2023

  28. [36]

    Natural language pro- cessing for ehr-based computational phenotyping

    Zeng, Z., Deng, Y ., Li, X., Naumann, T., and Luo, Y . Natural language pro- cessing for ehr-based computational phenotyping. IEEE/ACM transactions on computational biology and bioinformatics 16 , 1 (2018), 139–153

    2018

  29. [37]

    Curriculum learning for graph neural net- works: Which edges should we learn first

    Zhang, Z., Wang, J., and Zhao, L. Curriculum learning for graph neural net- works: Which edges should we learn first. Advances in Neural Information Processing Systems 36 (2024)

    2024

  30. [38]

    Class noise vs

    Zhu, X., and Wu, X. Class noise vs. attribute noise: A quantitative study. Artificial intelligence review 22 (2004), 177–210. VOLUME 11, 2023 11 RUHAAN SINGH is currently a research intern at the Oak Ridge National Laboratory, Oak Ridge, Tennessee and a student at Farragut Hig...

    2004

Pith tools

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