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REVIEW 5 major objections 6 minor 37 references

Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims a single training objective can correct both image-quality shifts and class imbalance, improving embryo-stage classification by up to 25.19 percentage points over a strong adversarial baseline.

desk verdict Useful engineering combination for embryo domain adaptation, but the empirical protocol is broken: target test sets were used for hyperparameter selection, and the abstract's AUC claim is contradicted by Table 1. read the letter →

arxiv 2501.04958 v1 pith:FGBL5OEY submitted 2025-01-09 cs.CV cs.AI

Lei Li , Xinglin Zhang , Jun Liang , Tao Chen This is my paper · ORCID
classification cs.CVcs.AI
keywords domainshiftclassimbalanceadaptationembryodevelopmentassessmentmedicalimagingadversarialtrainingadaptivethresholdclass-specificattention
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 tries to show that the two obstacles to deploying embryo-assessment models—domain shift (moving from clinical time-lapse systems to cheaper microscopes) and class imbalance (blastocyst versus non-blastocyst proportions varying from 28.9% to 81.2%)—are best solved together, not separately. It proposes IADA, which couples class-specific attention in the feature extractor, class-weighted adversarial domain alignment, and adaptive classification thresholds, and proves a generalization bound that splits the target error into class-proportion mismatch plus class-weighted domain discrepancy. On four imaging modalities, IADA reports accuracy gains up to 25.19 percentage points and AUC gains up to 12.56 percentage points over the MD-Net baseline, with balanced precision and recall. A sympathetic reader would care because if the claim holds, inexpensive portable and smartphone microscopes could produce embryo assessments close to clinical time-lapse quality, with minority classes not sacrificed.

What carries the argument

The load-bearing object is the three-part IADA training objective (10): classification with weighted focal loss, adversarial domain alignment with class-specific instance weights, and regularization. The mechanism that carries the argument is the coupling of three formulas: the class-attention feature combination $z_i = \sum_c \alpha_c(x_i) f_c(x_i)$ (Eq. 3), the class-weighted adversarial loss $L_{adv}$ (Eqs. 4–6), and the adaptive threshold decision $\hat{y} = \arg\max_c (C_\psi(z)_c - \tau_c)$ (Eq. 8), with $\tau_c = \beta \log(n^s_c / \min_k n^s_k) + \gamma$. Theorem 5.1 is the identity that ties the story together: target error is bounded by source error plus the $\ell^1$ difference of class proportions plus class-minimum-weighted domain discrepancies plus ideal joint error, so imbalance and shift are not separable nuisances but additive terms in the same bound.

What would settle it

Re-run the ED4→ED3 transfer with ResNet-50 for five seeds and compute each seed's accuracy: if the spread of IADA accuracies overlaps the MD-Net range, or if the standard error of the 0.9457 − 0.6938 = 0.2519 difference exceeds roughly 0.05, the headline improvement is not statistically distinguishable.

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Extended reading notes

Core claim

The central claim is that a domain-adaptation framework can be made imbalance-aware by construction. IADA's feature extractor computes per-class feature adapters and attends over them, its discriminator is trained with class-balanced sampling and instance weights inverse to source class frequency, and its classifier subtracts learnable class-dependent thresholds before the argmax. The paper derives a generalization bound $\epsilon_t(h) \leq \epsilon_s(h) + \sum_i |\pi^s_i-\pi^t_i| + \sum_i \min(\pi^s_i,\pi^t_i)d_i(H) + \lambda$, arguing that both class-proportion shifts and domain discrepancy contribute additively to target error, and gives convergence and complexity results for the training objective. Experiments compare IADA to MD-Net on ResNet-50, Inception v3, and Xception across transfers ED4→ED4, ED4→ED3, ED4→ED2, and ED4→ED1; the largest reported win is ED4→ED3 with ResNet-50, where accuracy rises from 0.6938 to 0.9457.

Load-bearing premise

The performance claims stand on the assumption that the unreported seed-to-seed variability of Table 1 is small, because the section promises five random seeds and coefficient of variation yet reports single numbers on tiny target sets (69 and 258 images).

Editorial extensions

If this is right

  • If the reported numbers hold, models trained on one clinical time-lapse system can be transferred to portable and smartphone microscopes while keeping accuracy above 0.84 in most tested settings, instead of degrading toward random-level performance.
  • Minority-class performance in low-quality domains improves: in ED4→ED2 with Xception, IADA reaches perfect recall while holding precision near 0.89, which matters when non-blastocyst embryos are the rare class.
  • The generalization bound implies that domain adaptation alone cannot erase the cost of label shift; deployments must also measure or estimate target class proportions, since $\sum_i |\pi^s_i-\pi^t_i|$ appears additively.
  • Because the balanced-domain corollary removes the proportion-difference term, the framework predicts that deliberately balancing source data or reweighting it toward target proportions should shrink the domain gap, a testable design rule.

Reading between the lines

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

  • The same additive-bound logic suggests a stress test beyond embryo images: in any clinically imbalanced classification task, the framework should show its largest gains exactly when source and target class proportions diverge most; if not, the mechanism's specific contribution is weaker than claimed.
  • The paper does not use target-domain labels, so IADA's adaptive thresholds can only be tuned to source frequencies; an implied extension is to replace the source-frequency threshold with an online estimate of target proportions, which the bound suggests would further reduce error.
  • Because the target test sets are very small (69 and 258 images), the advertised percentage-point differences should be read as provisional; a direct extension is to pool or enlarge the target sets and check whether the 12.56-point AUC improvement survives.
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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 manuscript proposes Imbalance-Aware Domain Adaptation (IADA), a framework for embryo-development image classification that combines class-specific attention, class-weighted adversarial alignment, and adaptive decision thresholds. It presents a theoretical analysis with generalization, convergence, and complexity bounds, and evaluates the method on one source domain (ED4, clinical time-lapse) and three target domains (ED3, ED2, ED1) of decreasing image quality, comparing against MD-Net with ResNet-50, Inception v3, and Xception. The abstract claims accuracy gains up to 25.19% and AUC gains up to 12.56%. The central empirical claim is not supported as reported because the key hyperparameters are selected on the target test sets, the promised five-seed variance statistics are absent, and some table entries contradict the abstract's blanket AUC claim.

Significance. If the claimed results survived a sound evaluation, IADA would be a practically useful contribution to domain adaptation under class imbalance in medical imaging, and the public code availability would aid reproducibility. The paper also attempts a formal treatment of class-imbalance-aware generalization and convergence, which is a meaningful direction. However, the current significance is limited by a test-set selection protocol that invalidates the headline numbers, by missing variance estimates on very small target test sets, and by a theoretical analysis that is not connected to the experiments in a predictive way. The positive results in some settings suggest the method may have merit, but the present evidence is insufficient to support the paper's central claims.

major comments (5)
  1. [§6.1, §6.3, Table 1, Fig. 2] Section 6.1 states that the target-domain datasets are 'reserved entirely for testing,' but the same section says λreg and λadv are chosen by a line search and refers to Section 6.3. Section 6.3 (Fig. 2) plots AUC on ED4→ED3, ED4→ED2, and ED4→ED1 as functions of λreg and λadv; these are the same target test sets used in Table 1. Selecting hyperparameters from these curves makes the Table 1 results post-selection estimates rather than independent generalization measurements. This is load-bearing because the headline 25.19% accuracy gain on ED4→ED3 with ResNet-50 (0.9457 vs. 0.6938) and the large ablation swings in Fig. 2 could be produced by picking a favorable λ per setting. The experiments should be redone with hyperparameters selected on a source-only validation set or a separate target validation set, and the exact selection rule must be reported.
  2. [§6.1, Table 1] The text promises that all experiments are repeated with five different random seeds and that mean performance and coefficient of variation are reported, but Table 1 contains only single point estimates. With ED2 containing 69 images and ED3 containing 258 images, the 25.19 percentage-point accuracy difference corresponds to roughly 65 images and the ED4→ED2 differences to only a few images, so seed-to-seed variability could be decisive. The authors should report the five-seed means and standard deviations or CVs, and ideally confidence intervals, before any comparative claim can be assessed.
  3. [Abstract, Table 1] The blanket claim of AUC improvement is contradicted by the table. For Inception v3, IADA has AUC 0.3750 versus MD-Net's 0.6442 on ED4→ED2 and 0.4543 versus 0.6025 on ED4→ED1, and Xception on ED4→ED4 is also slightly below MD-Net (0.9386 vs. 0.9415). The 'up to 12.56%' AUC improvement is therefore a selected favorable case rather than a verified general result. The abstract should be qualified by architecture and setting, or the authors should provide a statistically valid aggregated comparison.
  4. [§5, §6.3] The theoretical results do not provide a predictive account of the experiments. Section 6.3 explains the ablation curves using d_i(H) from Theorem 5.1 and the gradient bound G from Theorem 5.7, but neither quantity is measured, and Theorem 5.7's Assumption 5.5 (β-smoothness and μ-strong convexity) is not satisfied by the deep networks used in the experiments. The claim that a high λadv degrades performance 'because G increases' is therefore not tested. The authors should either measure the relevant quantities, derive a falsifiable prediction from the bounds, or explicitly present the theory as independent optimization-theoretic guarantees rather than as an explanation of the experimental behavior.
  5. [§5, Theorem 5.15] Theorem 5.15 is mathematically incorrect as stated. Minimizing Σ_i 1/b_i subject to Σ_i b_i = B gives all batch sizes equal, b_i = B/C, not the square-root dependence on min(πs_i, πt_i) claimed in the theorem. The Lagrangian derivation in the proof does not yield the stated formula, so the theorem and its proof need to be corrected or the optimization problem restated.
minor comments (6)
  1. [Abstract] The phrase 'The code is made public available' should be 'The code is made publicly available.'
  2. [Fig. 2] The caption labels both panels as '(a)' and says both show λreg; the second panel is discussed in the text as varying λadv and should be labeled '(b)' accordingly.
  3. [References] Several references are duplicated: [8] and [21] are the same Rubio et al. paper, [5] and [22] are the same Dimitriadis et al. paper, and [9] and [23] are the same Zaninovic and Rosenwaks paper; these should be merged.
  4. [References] Reference [38] cites a flower-classification tutorial rather than the original Inception-v3 architecture paper by Szegedy et al.; please cite the primary source.
  5. [§4.4, §6.1] Equation (12) introduces λ1, λ2, λ3 and the terms Lcons and Ldiv without defining them or connecting them to the λreg reported in Section 6.1; please specify the objective actually implemented.
  6. [Table 1] Only MD-Net is compared in Table 1; given the abstract's reference to 'existing methods' in the plural, at least one additional standard domain-adaptation or class-imbalance baseline should be included for a meaningful comparison.

Circularity Check

1 steps flagged · score 6.0 of 10

Central empirical claims are selected on the target test sets via the Section 6.3 line search, so the headline improvements are fitted rather than predicted.

  1. fitted input called prediction [Section 6.1 (Training Protocol and Implementation Details); Section 6.3 (Ablation Study) and Fig. 2]
    "The target domain datasets are reserved entirely for testing to evaluate domain adaptation performance under different imbalance scenarios. [...] The key hyperparameters of regularization coefficient λreg and adversarial coefficient λadv are selected according to their performance in a line search. More details can be found in Section 6.3. [...] The ablation study presented in Fig. 2 investigates the sensitivity of the model to two key hyperparameters: the regularization coefficient λreg and the adversarial coefficient λadv."

    The 'target domain datasets' that are 'reserved entirely for testing' are the same ED3, ED2, and ED1 sets whose AUC curves in Fig. 2 are used to select λreg and λadv via a line search. Therefore the Table 1 numbers, including the headline ResNet-50 ED4→ED3 accuracy gain of 0.9457 versus 0.6938 (25.19%) and the advertised low-quality AUC improvements, are selected on the evaluation data rather than predicted independently. The Section 6.1 promise to report five-seed means and coefficients of variation is not honored, so no variance estimate bounds the selection effect. The central generalization claim is thus, by the paper's own protocol, a fit to the target test sets.

full rationale

Pattern 2 applies at the evaluation level: the paper states that target domains are reserved entirely for testing, yet Section 6.3 plots target-scenario AUC curves for ED4→ED3, ED4→ED2, and ED4→ED1 across λreg and λadv, and Section 6.1 says these coefficients are selected by a line search. Hence the Table 1 accuracies, including the 25.19% ED4→ED3 ResNet-50 gain, are the result of picking hyperparameters on the same evaluation sets, so the advertised generalization improvement reduces to a fit rather than an independent prediction. The promised five-seed means and coefficients of variation are never reported, leaving no variance estimate to bound this selection. The theoretical analysis is not used to derive the empirical numbers; the ablation explanations via unmeasured quantities d_i(H) and G are post-hoc narratives rather than circular derivations. The self-citations to references [7, 11, 13, 28] are bibliographic and do not carry the central argument. Therefore the score reflects one central fitted-input-as-prediction step, not pervasive circularity.

Assumptions & free parameters 8 free parameters · 3 assumptions · 0 invented entities

The central empirical claim depends on many hand-chosen hyperparameters and on unmeasured theoretical quantities. The convergence proof assumes smoothness and strong convexity, which the actual objective does not satisfy. No new physical or conceptual entity is introduced.

free parameters (8)
  • lambda_reg = not reported (line search)
    Regularization coefficient in Eq. 10, selected by line search in Section 6.1; values not reported.
  • lambda_adv = not reported (line search)
    Adversarial coefficient in Eq. 10, selected by line search; values not reported.
  • lambda0 = not reported
    Initial adversarial weight in warm-up schedule Eq. 13, chosen by hand.
  • beta = learnable, not reported
    Threshold parameter in Eq. 7, learned during training.
  • gamma (threshold) = learnable, not reported
    Threshold offset in Eq. 7, learned during training; same symbol is also used as focal loss exponent in Eq. 11, creating ambiguity.
  • T = learned, not reported
    Temperature for confidence calibration in Eq. 9, learned during training.
  • focal loss gamma = not stated
    Exponent in focal loss Eq. 11; no value is given, likely set by convention.
  • training hyperparameters = lr=0.001, batch=2, wd=5e-4, 50k iterations
    Fixed training settings in Section 6.1; central to reproducing results.
assumptions (3)
  • ad hoc to paper The loss is beta-smooth and mu-strongly convex (Assumption 5.5).
    Used to prove convergence in Theorem 5.7, but the actual IADA objective in Eq. 10 is a non-convex adversarial min-max problem with attention and thresholds; this assumption is false for deep networks.
  • domain assumption Existence of class-conditional discrepancy d_i(H) and ideal joint error lambda bounding |epsilon_t,i(h) - epsilon_s,i(h)| (Theorem 5.1).
    The generalization bound relies on unmeasured terms d_i(H) and lambda that are not estimated, making the bound non-quantitative.
  • domain assumption Class proportions pi^s_i and pi^t_i are known and fixed (Definition 5.11, Assumption 5.5).
    The method uses source class frequencies for weights; target proportions are known only at evaluation, not during training.

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Pith. "Pith review of Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment." pith.science (2026). https://pith.science/paper/FGBL5OEY

@misc{pith2026250104958,
  author       = {Pith},
  title        = {Pith review of: Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FGBL5OEY}},
  note         = {Machine review of arXiv:2501.04958}
}
read the original abstract

Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and class imbalance, where certain disease conditions are naturally underrepresented. We present Imbalance-Aware Domain Adaptation (IADA), a novel framework that simultaneously tackles both challenges through three key components: (1) adaptive feature learning with class-specific attention mechanisms, (2) balanced domain alignment with dynamic weighting, and (3) adaptive threshold optimization. Our theoretical analysis establishes convergence guarantees and complexity bounds. Through extensive experiments on embryo development assessment across four imaging modalities, IADA demonstrates significant improvements over existing methods, achieving up to 25.19\% higher accuracy while maintaining balanced performance across classes. In challenging scenarios with low-quality imaging systems, IADA shows robust generalization with AUC improvements of up to 12.56\%. These results demonstrate IADA's potential for developing reliable and equitable medical imaging systems for diverse clinical settings. The code is made public available at \url{https://github.com/yinghemedical/imbalance-aware_domain_adaptation}

Figures

Figures reproduced from arXiv: 2501.04958 by the authors.

Figure 1
Figure 1. Illustration of the impact of class imbalance on domain shift in embryo classification. The fig [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Ablation study of the effects of regularization coefficient [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

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Works this paper leans on

37 extracted references · 33 canonical work pages

  1. [1]

    A survey on deep learning in medical image analysis,

    G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,”Medical Image Analysis, vol. 42, pp. 60–88, 2017

    work page 2017

  2. [2]

    Training cost-sensitive neural networks with methods addressing the class imbalance problem,

    Z.-H. Zhou and X.-Y. Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on knowledge and data engineering , vol. 18, no. 1, pp. 63–77, 2006

    work page 2006

  3. [3]

    Domain-adversarial training of neural networks,

    Y. Ganin and V. Lempitsky, “Domain-adversarial training of neural networks,” Journal of Machine Learning Research, vol. 17, no. 1, pp. 2096–2030, 2016

    work page 2016

  4. [4]

    Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images,

    M. K. Kanakasabapathy, P. Thirumalaraju, H. Kandula, F. Doshi, A. D. Sivakumar, D. Kartik, R. Gupta, R. Pooniwala, J. A. Branda, A. M. Tsibris et al. , “Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images,” Nature biomedical engineering , vol. 5, no. 6, pp. 571–585, 2021

    work page 2021

  5. [6]

    Smote: Synthetic minority over-sampling technique,

    N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research , vol. 16, pp. 321–357, 2002

    work page 2002

  6. [7]

    MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder

    L. Li, T. Zhang, X. Zhang, J. Liu, B. Ma, Y. Luo, and T. Chen, “Medflip: Medical vision-and-language self-supervised fast pre-training with masked autoencoder,” arXiv preprint arXiv:2403.04626 , 2024

    work page Pith review arXiv 2024

  7. [10]

    Adversarial discriminative domain adaptation,

    E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” CVPR, 2017

    work page 2017

  8. [11]

    Segment any building,

    L. Li, “Segment any building,” in Computer Graphics International Conference . Springer, 2023, pp. 155– 166. 13

    work page 2023

Show all 37 references
  1. [12]

    Adasyn: Adaptive synthetic sampling approach for imbalanced learning,

    H. He and Y. Bai, “Adasyn: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 2008, pp. 1322–1328

    2008

  2. [13]

    Mamba-unet: Unet-like pure visual mamba for medical image segmentation,

    Z. Wang, J.-Q. Zheng, Y. Zhang, G. Cui, and L. Li, “Mamba-unet: Unet-like pure visual mamba for medical image segmentation,” arXiv preprint arXiv:2402.05079 , 2024

    2024 arXiv

  3. [14]

    Embryo selection through artificial intelligence versus embryologists: a systematic review,

    M. Salih, C. Austin, C. Warty, D. Tiktin, D. Rolnik, M. Momeni et al., “Embryo selection through artificial intelligence versus embryologists: a systematic review,” Human Reproduction Open , vol. 2023, no. 3, p. hoad031, 2023

    2023

  4. [15]

    Dataset shift in machine learning,

    J. Quinonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, “Dataset shift in machine learning,” MIT Press, 2008

    2008

  5. [16]

    Unbiased look at dataset bias,

    A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” CVPR, pp. 1521–1528, 2011

    2011

  6. [17]

    Not-so-supervised: A survey of semi-supervised, multi- instance, and transfer learning in medical image analysis,

    V. Cheplygina, M. de Bruijne, and J. P. Pluim, “Not-so-supervised: A survey of semi-supervised, multi- instance, and transfer learning in medical image analysis,” Medical Image Analysis , vol. 54, pp. 280–296, 2019

    2019

  7. [18]

    Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,

    Y. Liu, J. Cheng, X. Zhang, S. K. Leung, X. Xu, Y. Li, and D. Shen, “Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,” Medical Image Analysis , vol. 66, p. 101859, 2020

    2020

  8. [19]

    Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss,

    Q. Dou, C. Ouyang, H. Chen, and P.-A. Heng, “Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss,” IEEE Transactions on Medical Imaging , vol. 37, no. 5, pp. 1342–1351, 2018

    2018

  9. [20]

    Optimal transport driven deep learning with emphasis on pathology images,

    M. Aburidi, “Optimal transport driven deep learning with emphasis on pathology images,” 2023

    2023

  10. [21]

    Clinical validation of embryo culture and selection by morphokinetic analysis: a randomized, controlled trial of the embryoscope,

    I. Rubio, A. Gal´ an, Z. Larreategui, F. Ayerdi, J. Bellver, J. Herrero, and M. Meseguer, “Clinical validation of embryo culture and selection by morphokinetic analysis: a randomized, controlled trial of the embryoscope,” Fertility and sterility , vol. 102, no. 5, pp. 1287–1294, 2014

    2014

  11. [22]

    Artificial intelligence in the embryology laboratory: a review,

    I. Dimitriadis, N. Zaninovic, A. C. Badiola, and C. L. Bormann, “Artificial intelligence in the embryology laboratory: a review,” Reproductive biomedicine online, vol. 44, no. 3, pp. 435–448, 2022

    2022

  12. [23]

    Artificial intelligence in human in vitro fertilization and embryology,

    N. Zaninovic and Z. Rosenwaks, “Artificial intelligence in human in vitro fertilization and embryology,” Fertility and Sterility , vol. 114, no. 5, pp. 914–920, 2020

    2020

  13. [24]

    Embryo selection through artificial intelligence versus embryologists: a systematic review,

    M. Salih, C. Austin, R. Warty, C. Tiktin, D. Rolnik, M. Momeni, H. Rezatofighi, S. Reddy, V. Smith, B. Vollenhoven et al. , “Embryo selection through artificial intelligence versus embryologists: a systematic review,” Human Reproduction Open, vol. 2023, no. 3, p. hoad031, 2023

    2023

  14. [25]

    Medical image synthesis for data augmentation and anonymization using generative adversarial networks,

    H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” in Simulation and Synthesis in Medical Imaging: Thi...

    2018

  15. [26]

    Springer, 2018, pp. 1–11

    2018

  16. [27]

    Domain adaptation for medical image analysis: a survey,

    H. Guan and M. Liu, “Domain adaptation for medical image analysis: a survey,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 3, pp. 1173–1185, 2021

    2021

  17. [28]

    Unpaired image-to-image translation using cycle-consistent adversarial networks,

    J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proceedings of the IEEE international conference on computer vision , pp. 2223–2232, 2017

    2017

  18. [29]

    Cpseg: Finer-grained image semantic segmentation via chain-of-thought language prompting,

    L. Li, “Cpseg: Finer-grained image semantic segmentation via chain-of-thought language prompting,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 513–522

    2024

  19. [30]

    Cluster-based under-sampling approaches for imbalanced data distributions,

    S. Yen and Y.-S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,” Expert systems with applications , vol. 36, no. 3, pp. 5718–5727, 2009

    2009

  20. [31]

    Training cost-sensitive neural networks with methods addressing the class imbalance problem,

    Z. Zhou and X. Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on Knowledge and Data Engineering , vol. 18, no. 1, pp. 63–77, 2006

    2006

  21. [32]

    Rusboost: A hybrid approach to alleviating class imbalance,

    C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,” in 2010 IEEE International Conference on Data Mining . IEEE, 2010, pp. 337–342

    2010

  22. [33]

    A study of the behavior of several methods for balancing machine learning training data,

    G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD Explorations Newsletter , vol. 6, no. 1, pp. 20–29, 2004

    2004

  23. [34]

    Density-aware personalized training for risk prediction in imbalanced medical data,

    Z. Huo, X. Qian, S. Huang, Z. Wang, and B. J. Mortazavi, “Density-aware personalized training for risk prediction in imbalanced medical data,” in Machine Learning for Healthcare Conference. PMLR, 2022, pp. 101–122. 14

    2022

  24. [35]

    Fediic: Towards robust federated learning for class- imbalanced medical image classification,

    N. Wu, L. Yu, X. Yang, K.-T. Cheng, and Z. Yan, “Fediic: Towards robust federated learning for class- imbalanced medical image classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2023, pp. 692–702

    2023

  25. [36]

    Progressive class-center triplet loss for imbalanced medical image classification,

    Y. Li et al., “Progressive class-center triplet loss for imbalanced medical image classification,” arXiv preprint arXiv:2207.04793, 2022

    2022 arXiv

  26. [37]

    Learning from class-imbalanced data: Review of methods and applications,

    G. Haixiang et al. , “Learning from class-imbalanced data: Review of methods and applications,” Expert Systems with Applications , vol. 73, pp. 220–239, 2017

    2017

  27. [38]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

    2016

  28. [39]

    Inception-v3 for flower classification,

    X. Xia, C. Xu, and B. Nan, “Inception-v3 for flower classification,” in 2017 2nd international conference on image, vision and computing (ICIVC) . IEEE, 2017, pp. 783–787

    2017

  29. [40]

    Xception: Deep learning with depthwise separable convolutions,

    F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1251–1258. 15

    2017

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