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REVIEW 5 major objections 6 minor 30 references

ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read ADAptation selects breast ultrasound samples for annotation after reconstructing them in source style, reaching 0.8081 average accuracy at a 20% annotation budget across five classifiers.

desk verdict A genuinely novel assembly of diffusion reconstruction, hypersphere contrastive learning, and dual-scoring for active domain adaptation, but the paper's own Table 2 contradicts its central claim at 30% and 50% annotation budgets. read the letter →

arxiv 2507.00474 v1 pith:XI6FNY5P submitted 2025-07-01 cs.CV

Yaofei Duan , Yuhao Huang , Xin Yang , Luyi Han , Xinyu Xie , Zhiyuan Zhu , Ping He , Ka-Hou Chan
show 4 more authors
Ligang Cui Sio-Kei Im Dong Ni Tao Tan
This is my paper
classification cs.CV
keywords activelearningdomainadaptationdiffusionmodelcontrastivebreastultrasoundmedicalimageclassificationsampleselection
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

This paper is trying to establish that active learning can be made to work across hospital-to-hospital shifts in breast ultrasound images if the domain gap is removed before sample selection. It proposes ADAptation, which translates unlabeled target images into source-domain style with a diffusion model, learns compact hypersphere-constrained embeddings of originals and reconstructions, and ranks images by a combined uncertainty-representativeness score. On four breast ultrasound datasets and five classifiers, the authors report that selecting the top 20% of samples this way reaches an average accuracy of 0.8081, beating six active-learning baselines by a margin they report as significant ($p < 0.01$). If correct, the framework would let a clinical site annotate only a small, well-chosen fraction of new-scanner data and then fine-tune several diagnostic models at once.

What carries the argument

The machinery is a three-stage pipeline. Stage I fine-tunes a diffusion model on the labeled source dataset, conditioned on a text prompt and on each image's canny edge map, so the model learns the source appearance while keeping the target's edge structure. Stage II feeds each unlabeled target image through the frozen diffusion model to produce a source-style reconstruction. Stage III uses a teacher-student contrastive network with a convolutional backbone to project each original image and its reconstruction onto the unit sphere in $\mathbb{R}^{256}$ via $L^2$ normalization, and pulls each pair together with the angular contrastive loss $L = \frac{1}{N}\sum_i \left(m \cdot \arccos\left(\hat{f}(x_i^u) \cdot g'(x_i^r)\right)\right)^2$ with scaling factor $m=4$. The selection score is $I_i = \min_{p,q} |\theta_p - \theta_q| + \omega \cdot \mathrm{SphericalDist}(x_i^u, x_i^r)$, where the first term is KNN-based uncertainty (small when the sample sits between cluster centroids) and the second is representativeness (the spherical distance from an image to its reconstruction); the samples with the smallest scores are sent for annotation.

What would settle it

A direct test would be to check, for each target image, whether the lesion is preserved in its reconstruction (for example by comparing lesion masks or edge maps before and after reconstruction) and then see whether the samples ADAptation ranks as most representative are the ones with preserved lesions. If images whose reconstructions erase the lesion are selected as representative, or if filtering out low-fidelity reconstructions (for instance PSNR below 10 or multi-scale structural similarity below 0.2) improves downstream accuracy, then the reconstruction prior is carrying the selection signal in a different way than claimed.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that reconstruction is a usable prior for unsupervised active learning under domain shift. Instead of decorating features or relying on a downstream model's confidence, ADAptation asks a source-trained diffusion model to redraw each target image in source style, then aligns original and reconstruction in a normalized hyperspherical embedding space. Informativeness is a single score made of two terms: an uncertainty term that measures how ambiguous a sample is relative to the cluster centroids, and a representativeness term that measures how far the sample has moved from its reconstruction. The headline quantitative claim is that this procedure, applied once at a 20% annotation budget, yields an average accuracy of 0.8081 across five classifiers on target breast ultrasound test sets, improving on the second-best active-learning method by 4.83% as the paper reports it, and it remains the most accurate method at 30% and 50% budgets.

Load-bearing premise

The method's load-bearing premise is that source-guided diffusion reconstruction keeps the diagnostically relevant content of each target ultrasound image intact, so that the contrastive alignment and the reconstruction-distance representativeness score are built on clinically meaningful structure rather than on style-transfer artifacts.

Editorial extensions

If this is right

  • A single annotation round selected by ADAptation improves all five tested classifiers over the six compared active-learning baselines at a 20% budget, suggesting the selection transfers across model architectures.
  • Because selection happens once and is not tied to a particular downstream model, the framework matches the clinical workflow of rapidly updating several diagnostic models with new data.
  • At an 80% annotation ratio ADAptation reaches an average accuracy of 0.9351, approaching the full-data upper bound of 0.9435, while several baselines perform worse than random sampling at that budget.
  • The ablation results indicate that contrastive learning, the hypersphere constraint, and the reconstruction prior each contribute to the gain, and that four cluster centroids give the best uncertainty estimate.

Reading between the lines

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

  • Editorial inference: The paper's own reconstruction metrics show low pixel-level fidelity on target domains, so the mechanism that helps selection is likely structural rather than photometric; a targeted ablation varying reconstruction fidelity while holding the selection rule fixed would make this explicit.
  • Editorial inference: Because the representativeness term relies only on the spherical distance between an image and its source-style reconstruction, the method may transfer to other ultrasound tasks or even other imaging modalities where edge structure carries diagnostic information, though the paper does not test this.
  • Editorial inference: The uncertainty score is computed in embedding space rather than from any classifier's confidence, which may explain why one selection set works across five architectures; this property could also make the framework useful for model-agnostic data curation beyond the four datasets studied.
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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 ADAptation, an unsupervised active learning (AL) method for cross-domain breast ultrasound classification. It first fine-tunes a ControlNet-guided diffusion model on the source domain, reconstructs target images in source style, learns hypersphere-constrained contrastive embeddings of target images and their reconstructions, and then ranks unlabeled target samples by a dual uncertainty-representativeness score. The selected samples are used to fine-tune five classifiers, evaluated on three target datasets at 20%, 30%, 50%, and 80% annotation budgets. The paper reports the best performance at 20% (0.8081 average accuracy) and claims superiority over AL baselines across all budgets.

Significance. Adapting breast ultrasound classifiers to new scanners or hospitals with minimal labeling is clinically relevant, and the combination of diffusion-based style transfer with AL is an interesting idea. The evaluation is broad: three public and one internal dataset, five classifiers, and four annotation budgets, with code released. However, the central claim of superiority is not supported by the paper's own Table 2 at 30% and 50% budgets, and the 80% comparison lacks baseline rows. The reconstruction fidelity results in Table 4 raise additional doubts about whether the representativeness score is measuring clinical content. The method also leaves the dual-score weight in Eq. (4) unspecified, and the cluster-number selection appears to use the test sets. These issues are load-bearing, so the contribution is not established as presented.

major comments (5)
  1. [Table 2] At 30% annotation, ADAptation averages 0.8213, while Random reaches 0.8585, Max-Entropy 0.8689, and LfOSA 0.8744; at 50%, ADAptation averages 0.8814, while Random reaches 0.9232, VAAL 0.9090, and LfOSA 0.9188. The text in Section 3 claims 'average improvements of 3.95% and 2.87%' at these budgets, which directly contradicts the table. Since the abstract and conclusion claim that ADAptation 'surpasses existing strong AL-based competitors,' this internal contradiction undermines the central claim. Please verify the numbers, rerun the comparison if necessary, and reconcile the text with the table.
  2. [Eq. (4)] The dual-scoring equation is underspecified. The weight omega appears with no value, range, or selection procedure, although the dual-score balance is a key contribution. In addition, the uncertainty term is written as argmin over p,q of |theta_p - theta_q|, i.e., the minimum pairwise angular difference, whereas the text describes the absolute difference between the smallest and largest angular differences (max - min). These are different quantities, and the equation should match the described algorithm.
  3. [Table 2, 80% block] The 80% annotation block reports only the ADAptation row and no baseline or random-sampling rows. Consequently, the claim that ADAptation 'approaches the upper bound (0.9435)' and the discussion contrasting ADAptation with other methods at high budgets cannot be checked. Please include all baseline rows at every budget.
  4. [Table 4 and Section 2.1] The reconstruction stage, which supplies the representativeness prior, has low fidelity on target domains: PSNR 10.40 to 11.89, MS-SSIM 0.23 to 0.37, and RMSE increases of 14.80 to 24.38 relative to source. If reconstructions distort lesion-relevant content, the SphericalDist term in Eq. (4) may measure reconstruction error rather than source-domain divergence. The assertion that reconstructions preserve 'critical medical characteristics' should be validated with diagnostic or classification-level metrics, not only pixel-level metrics.
  5. [Table 3] The ablation table is evaluated 'test on the target domain sets,' and the cluster number (4) that gives the best average accuracy on those test sets is then used in the main comparison in Table 2 on the same test sets. This introduces selection bias into the headline numbers; hyperparameters should be chosen on a validation split, or the test-set reuse should be justified and its effect quantified.
minor comments (6)
  1. [Section 2.2] The text introduces teacher and student networks as \hat{f} and g', but Eq. (3) uses f and g without the hat or prime; also, the projection is described as a '255-dimensional hypersphere' while the embedding is z in R^256. Please align the notation.
  2. [Section 2.3] 'KNN clustering with k centroids' is not standard terminology; if the method uses k-means-like centroids, please state this explicitly.
  3. [Section 3] The claim of statistical significance (p < 0.01) is made only for the 20% budget, without stating the test used, the number of runs, or how multiple comparisons were handled.
  4. [Table 1] The 'Amount' column appears to contain two numbers per row (e.g., 210 and 437) without column labels, and the 'Domain Bias' values after homogenization are not explained in the caption.
  5. [Eq. (1)] The sampling procedure for reconstruction is not defined (e.g., DDPM or DDIM steps, guidance scale), which prevents reproducibility of the reconstruction stage.
  6. [Throughout] There are several presentation issues, including 'Domian' in Table 4 and 'we proposed ADAptation' in the introduction, which should be corrected.

Circularity Check

1 steps flagged · score 4.0 of 10

Cluster-number ablation on the target test sets contaminates the headline 20% accuracy; otherwise the pipeline is not circular.

  1. fitted input called prediction [Section 3, Ablation Study (Table 3 and Table 2)]
    "We also analyze the impact of cluster numbers on ADAptation's performance. Results indicate that four clusters yield optimal performance (0.8081), which we adopt for all subsequent experiments."

    Table 3 evaluates cluster numbers 2-5 'test on the target domain sets' and selects Cluster=4 because it yields 0.8081 average accuracy. This exact 0.8081 is then reported in Table 2 as ADAptation's 20%-budget accuracy and used for the claim 'ADAptation achieves an average accuracy of 0.8081, significantly surpassing all competitors (p<0.01)'. The headline number is therefore the maximum over the tested k values on the same test set that is later used for comparison; the test set has already influenced the choice of k, so the reported accuracy is not an independent evaluation. This is fitting a hyperparameter to the test set and then presenting the test-set result as the method's performance, a partial circularity in the main empirical claim at the 20% budget.

full rationale

The derivation chain is not equation-level circular: the diffusion reconstruction, contrastive loss, and dual-scoring are defined independently of the reported accuracies. No parameter in Eqs. (1)-(5) is defined in terms of the final test labels. The only circularity found is experimental: the cluster-number hyperparameter (k=4) was selected by comparing accuracy on the target-domain test sets (Table 3), and the resulting 0.8081 value is then reported in Table 2 as ADAptation's 20%-budget result and used as evidence of superiority. This means the headline 20% number is partially a result of test-set selection, not an independent estimate. At 30% and 50% budgets ADAptation is outperformed by Random in the very same Table 2; that contradiction is a correctness/falsification issue rather than circularity. Reconstruction fidelity concerns (Table 4) affect validity of the approach but do not create a definitional circularity. Overall, partial circularity in one headline number warrants score 4.

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

The empirical pipeline leans on assumptions about diffusion reconstruction fidelity and about the two proxy scores. It also requires tuning the scale of the contrastive loss, the number of clusters, and an unreported score weight. No new physical or algorithmic entities are introduced.

free parameters (3)
  • m (angular scaling factor in Eq. 3) = 4
    Chosen without sensitivity analysis or derivation; it scales the angular contrastive loss.
  • omega (dual-score weight in Eq. 4) = not specified
    The central selection score depends on this weight, but the paper never reports its value or an ablation over it.
  • k (number of KNN cluster centroids) = 4
    Selected via ablation on target test sets (Table 3), so the reported best result is tuned to the evaluation set.
assumptions (4)
  • domain assumption A diffusion model fine-tuned on source data, conditioned on canny edges and text, produces source-like reconstructions that preserve lesion content.
    Section 2.1; Table 4 shows low PSNR and MS-SSIM on target domains, so content preservation is not established.
  • ad hoc to paper The spherical distance between an image and its reconstruction measures representativeness relative to the source distribution.
    Section 2.3; used in Eq. (4) without validation or comparison to alternative representativeness measures.
  • ad hoc to paper The minimal angular gap to KNN centroids, as written in Eq. (4), is a valid uncertainty score.
    Section 2.3; text describes uncertainty as the absolute difference between smallest and largest angular differences, but Eq. (4) uses argmin over pairwise differences.
  • domain assumption Hypersphere projection of teacher and student embeddings prevents domain-specific bias.
    Section 2.2; the claim is plausible but not proven, and the text inconsistently says 255-dimensional and R^256.

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Cite this review

Pith. "Pith review of ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis." pith.science (2026). https://pith.science/paper/XI6FNY5P

@misc{pith2026250700474,
  author       = {Pith},
  title        = {Pith review of: ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XI6FNY5P}},
  note         = {Machine review of arXiv:2507.00474}
}
read the original abstract

Deep learning-based diagnostic models often suffer performance drops due to distribution shifts between training (source) and test (target) domains. Collecting and labeling sufficient target domain data for model retraining represents an optimal solution, yet is limited by time and scarce resources. Active learning (AL) offers an efficient approach to reduce annotation costs while maintaining performance, but struggles to handle the challenge posed by distribution variations across different datasets. In this study, we propose a novel unsupervised Active learning framework for Domain Adaptation, named ADAptation, which efficiently selects informative samples from multi-domain data pools under limited annotation budget. As a fundamental step, our method first utilizes the distribution homogenization capabilities of diffusion models to bridge cross-dataset gaps by translating target images into source-domain style. We then introduce two key innovations: (a) a hypersphere-constrained contrastive learning network for compact feature clustering, and (b) a dual-scoring mechanism that quantifies and balances sample uncertainty and representativeness. Extensive experiments on four breast ultrasound datasets (three public and one in-house/multi-center) across five common deep classifiers demonstrate that our method surpasses existing strong AL-based competitors, validating its effectiveness and generalization for clinical domain adaptation. The code is available at the anonymized link: https://github.com/miccai25-966/ADAptation.

Figures

Figures reproduced from arXiv: 2507.00474 by the authors.

Figure 1
Figure 1. Kernel Density Estimation (KDE) of the source domain and three target do￾mains. (a) Original data distributions. (b) Homogenized distributions after diffusion model-based reconstruction. shift [11,17]. This challenge is particularly pronounced in clinical settings, where variations in imaging equipments, scanning protocols, and patient populations across healthcare institutions significantly impact model performance… view at source ↗
Figure 2
Figure 2. Overview of the ADAptation framework for informative samples selection. is motivated by a key insight: while source and target data exhibit distinct distributional characteristics, diffusion models [8] can minimize domain-specific variations through reconstruction. Building upon this observation ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visualizations of Feature Projections: (a) Data embedding using KNN; (b) Embedding with Hypersphere constraint applied; (c) 3D spherical visualization result before clustering; (d) 3D spherical visualization result after clustering [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: T-SNE visualizations of different AL sampling strategies on breast US. The black, green, and yellow symbols represent the source, target, and selection data points. Qualitative analysis [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Works this paper leans on

30 extracted references · 14 canonical work pages

  1. [1]

    Data in brief28, 104863 (2020)

    Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in brief28, 104863 (2020)

    2020

  2. [2]

    arXiv preprint arXiv:1906.03671 (2019)

    Ash, J.T., Zhang, C., Krishnamurthy, A., Langford, J., Agarwal, A.: Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671 (2019)

    arXiv 2019

  3. [3]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Feng, W., Ju, L., Wang, L., Song, K., Zhao, X., Ge, Z.: Unsupervised domain adaptation for medical image segmentation by selective entropy constraints and adaptive semantic alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 623–631 (2023)

    work page 2023

  4. [4]

    Medical Physics51(4), 3110–3123 (2024)

    Gómez-Flores, W., Gregorio-Calas, M.J., Coelho de Albuquerque Pereira, W.: Bus- bra: A breast ultrasound dataset for assessing computer-aided diagnosis systems. Medical Physics51(4), 3110–3123 (2024)

    work page 2024

  5. [5]

    Advances in neural information processing systems33, 21271–21284 (2020)

    Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Do- ersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems33, 21271–21284 (2020)

    2020

  6. [6]

    IEEE Transactions on Biomedical Engineering69(3), 1173–1185 (2021)

    Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Transactions on Biomedical Engineering69(3), 1173–1185 (2021)

    2021

  7. [7]

    He,K.,Zhang,X.,Ren,S.,Sun,J.:Deepresiduallearningforimagerecognition.In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

    2016

  8. [8]

    Advances in neural information processing systems33, 6840–6851 (2020) 10 Y

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020) 10 Y. Duan et al

    work page 2020

Show all 30 references
  1. [9]

    arXiv preprint arXiv:1704.04861 (2017)

    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., An- dreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

    2017 arXiv

  2. [10]

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

    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)

    2017

  3. [11]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Huang, Y., Yang, X., Huang, X., Liang, J., Zhou, X., Chen, C., Dou, H., Hu, X., Cao, Y., Ni, D.: Online reflective learning for robust medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 652–662. Springer (2022)

    2022

  4. [12]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Huang, Y., Yang, X., Huang, X., Zhou, X., Chi, H., Dou, H., Hu, X., Wang, J., Deng, X., Ni, D.: Fourier test-time adaptation with multi-level consistency for robust classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. ...

    2023

  5. [13]

    arXiv preprint arXiv:2303.12484 (2023)

    Jin, C., Guo, Z., Lin, Y., Luo, L., Chen, H.: Label-efficient deep learning in medical image analysis: Challenges and future directions. arXiv preprint arXiv:2303.12484 (2023)

    2023 arXiv

  6. [14]

    arXiv preprint arXiv:2107.02331 (2021)

    Karamcheti, S., Krishna, R., Fei-Fei, L., Manning, C.D.: Mind your outliers! in- vestigating the negative impact of outliers on active learning for visual question answering. arXiv preprint arXiv:2107.02331 (2021)

    2021 arXiv

  7. [15]

    Advances in neural information pro- cessing systems32(2019)

    Kirsch, A., Van Amersfoort, J., Gal, Y.: Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information pro- cessing systems32(2019)

    2019

  8. [16]

    Computers in Biology and Medicine170, 107912 (2024)

    Kumari, S., Singh, P.: Deep learning for unsupervised domain adaptation in med- ical imaging: Recent advancements and future perspectives. Computers in Biology and Medicine170, 107912 (2024)

    2024

  9. [17]

    Medical Image Analysis p

    Lin, Z., Li, S., Wang, S., Gao, Z., Sun, Y., Lam, C.T., Hu, X., Yang, X., Ni, D., Tan, T.: An orchestration learning framework for ultrasound imaging: Prompt-guided hyper-perception and attention-matching downstream synchronization. Medical Image Analysis p. 103639 (2025)

    2025

  10. [18]

    Medical Image Anal- ysis83, 102655 (2023)

    Linmans, J., Elfwing, S., van der Laak, J., Litjens, G.: Predictive uncertainty esti- mation for out-of-distribution detection in digital pathology. Medical Image Anal- ysis83, 102655 (2023)

    2023

  11. [19]

    In: Proceedings of the European conference on computer vision (ECCV)

    Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV). pp. 116–131 (2018)

    2018

  12. [20]

    Medical image analysis97, 103261 (2024)

    Mahapatra, D., Tennakoon, R., George, Y., Roy, S., Bozorgtabar, B., Ge, Z., Reyes, M.: Alfredo: Active learning with feature disentangelement and domain adaptation for medical image classification. Medical image analysis97, 103261 (2024)

    2024

  13. [21]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Ning, K.P., Zhao, X., Li, Y., Huang, S.J.: Active learning for open-set annotation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 41–49 (2022)

    2022

  14. [22]

    arXiv preprint arXiv:1708.00489 (2017)

    Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core- set approach. arXiv preprint arXiv:1708.00489 (2017)

    2017 arXiv

  15. [23]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Sinha, S., Ebrahimi, S., Darrell, T.: Variational adversarial active learning. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 5972–5981 (2019)

    2019

  16. [24]

    In: International conference on machine learning

    Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019) ADAptation 11

    2019

  17. [25]

    Advances in neural information processing systems33, 19290–19301 (2020)

    Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. Advances in neural information processing systems33, 19290–19301 (2020)

    2020

  18. [26]

    IEEE journal of biomedical and health informatics22(4), 1218– 1226 (2017)

    Yap, M.H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A.K.,Marti,R.:Automatedbreastultrasoundlesionsdetectionusingconvolutional neural networks. IEEE journal of biomedical and health informatics22(4), 1218– 1226 (2017)

    2017

  19. [27]

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

    Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)

    2023

  20. [28]

    arXiv preprint arXiv:2303.00915 (2023)

    Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al.: Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915 (2023)

    2023 arXiv

  21. [29]

    Zhang, Y., Lu, Y., Xuan, Q.: How does contrastive learning organize images? In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 497–506 (2024)

    2024

  22. [30]

    IEEE Journal of Biomedical and Health Informatics (2024)

    Zhang, Z., Han, L., Zhang, T., Lin, Z., Gao, Q., Tong, T., Sun, Y., Tan, T.: Unimrisegnet: Universal 3d network for various organs and cancers segmentation on multi-sequence mri. IEEE Journal of Biomedical and Health Informatics (2024)

    2024

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