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REVIEW 5 major objections 5 minor 31 references

PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that an unsupervised pathology detector can be trained from as few as two healthy scans and still beat prior unsupervised methods at localizing brain tumors and chest diseases on BraTS2020 and ChestXray8.

desk verdict A plausible few-shot UPD method with a clean contrastive loss, but the evaluation protocol has enough holes that I'd want a revision before trusting the SOTA claim. read the letter →

arxiv 2505.17614 v1 pith:XDRBX4L3 submitted 2025-05-23 cs.CV

Sinchee Chin , Yinuo Ma , Xiaochen Yang , Jing-Hao Xue , Wenming Yang This is my paper · ORCID
classification cs.CV
keywords unsupervisedpathologydetectionfew-shotlearningcontrastivesyntheticembeddingsanomalymedicalimagingBraTS2020ChestXray8
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 establish that pathology detection in medical imaging does not need large healthy training sets or labeled disease examples. It introduces PathoSCOPE, which builds a normality reference from a few healthy scans and flags deviations as disease, claiming strong results with as few as two training scans on brain MRI and chest X-ray data. The method's two pillars are a contrastive loss that both compresses healthy-feature variation and enlarges deviations, and a synthesis module that invents pathological embeddings by pushing healthy features away along the discriminator's own gradient. If the claim holds, the practical bottleneck that hospitals lack representative healthy cohorts would be largely removed, and novel or rare diseases could be screened with minimal data.

What carries the argument

The carrying mechanism is a fixed prototypical anchor bank: a coreset-sampled set of healthy feature embeddings extracted by a pretrained ResNet18, which gives the model stable anatomical references. Two contrastive losses act on distances to the nearest anchor. The Local Contrastive Loss uses the Tritanh loss, $\mathcal{L}_C(d_\text{pull}, d_\text{push}) = \frac{e^{\lambda_0 d_\text{pull}} - e^{\lambda_1 d_\text{push}} + \epsilon}{e^{\lambda_0 d_\text{pull}} + e^{\lambda_1 d_\text{push}} + \epsilon}$, to pull non-pathological local embeddings toward the anchors and push local pathological embeddings (texture-overlaid patches) away. The Global Contrastive Loss does the same for whole-embedding distances, with global pathological embeddings produced by the PiEG module: twenty steps of normalized gradient ascent on the global loss, starting from Gaussian-perturbed healthy embeddings. The discriminator is then trained with binary cross-entropy on these synthetic positives and healthy negatives.

What would settle it

Run the same 2-shot protocol on a third public benchmark with real localized lesions (for example, lung nodules on CT) and compare against GLASS-m with the identical backbone and compute budget; if PathoSCOPE does not match or beat GLASS-m there, the claim that its contrastive anchoring and pathology-informed synthesis enable few-shot detection fails to generalize.

Watch

Extended reading notes

Core claim

On its own terms, the paper's claim is that few-shot unsupervised pathology detection can be made to work with as few as two non-pathological training scans. The method anchors healthy features to a prototypical bank, uses a local contrastive loss to suppress normal anatomical variation, and uses a global contrastive loss on gradient-ascent-synthesized pathological embeddings to sharpen the boundary between normal and diseased. Tested on BraTS2020 and ChestXray8 at 2, 4, 6, and 8 shots, PathoSCOPE reports the best image- and pixel-level scores among the unsupervised baselines it compares with, including 89.19% image AUROC on BraTS2020 at two shots and 72.23% on ChestXray8, at a cost of 2.48 GFLOPs and 166 FPS.

Load-bearing premise

The load-bearing premise is that the fake diseases the method creates—texture patches stuck onto healthy scans and healthy examples artificially altered to look abnormal—are close enough to real diseases that a model trained only on fakes will flag the real ones.

Editorial extensions

If this is right

  • Clinical sites with only a handful of normal scans per population could bootstrap a screening model without assembling large healthy cohorts.
  • Because outputs are pixel-level anomaly maps, the method can localize the suspicious region, not merely classify the image.
  • The training pipeline needs no pathology labels, so a new disease or imaging modality can be addressed as soon as two healthy examples are available.
  • Reported throughput of 166 FPS at 2.48 GFLOPs indicates the trained model could run in real time on clinical hardware, supporting use in resource-constrained settings.

Reading between the lines

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

  • Beyond the paper, the anchor-bank-plus-gradient-ascent recipe likely transfers to non-medical few-shot anomaly detection, such as inspecting manufactured parts with very few known-good samples, because it does not encode disease-specific priors.
  • The modality-dependent optimal perturbation strength (0.01 for brain MRI, 0.05 for chest X-ray) suggests a future adaptive scheduler could set synthesis difficulty per image or per region rather than as a global constant.
  • If the authors' stated limitation is taken seriously, replacing DTD texture overlays with a learned local generator trained from the discriminator's gradients could both remove reliance on an auxiliary texture dataset and improve anatomical realism.
  • The few-shot property may enable privacy-preserving multi-site collaboration, where sites share only compact healthy anchor banks instead of raw patient images.
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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 / 5 minor

Summary. The paper proposes PathoSCOPE, a few-shot unsupervised pathology detection framework that trains on only 2–8 non-pathological samples. The method combines a pre-trained feature extractor with a trainable feature adapter, a prototypical anchor bank, a Global-Local Contrastive Loss (GLCL), and a Pathology-informed Embedding Generation (PiEG) module that synthesizes pathological embeddings by gradient ascent on the discriminator loss. The authors report state-of-the-art results among unsupervised methods on BraTS2020 and ChestXray8, with image and pixel AUROC, PRO, and DICE metrics, as well as computational efficiency (2.48 GFLOPs, 166 FPS).

Significance. If the empirical claims hold, PathoSCOPE would be a meaningful step toward practical few-shot pathology detection, addressing the scarcity of healthy training data in medical imaging. The paper contributes a dual contrastive formulation that regularizes normal embeddings while synthesizing hard pathological embeddings, and it evaluates against a broad set of baselines on two medical datasets. The inclusion of efficiency metrics is a practical strength. However, the central claim of state-of-the-art few-shot performance rests on evaluation and hyperparameter-selection details that are currently underspecified, so the results are not yet fully supported.

major comments (5)
  1. [Section 3.1] The statement that training and test sets have 'no overlap' is insufficient for a few-shot normality-modeling claim. BraTS2020 is a glioma dataset without healthy control subjects; the manuscript does not state whether the 130 'healthy' test slices come from tumor patients (e.g., tumor-free slices from tumor patients) and whether patient identities are disjoint between training and test. If slices from the same patient appear in both training and test, the few-shot generalization results would be inflated. Please specify the exact construction of the healthy cohort and confirm patient-level disjointness.
  2. [Section 3.4 / Fig. 5] The perturbation strength η in Eq. (3) is selected per dataset by sweeping AUROC in Fig. 5, with no indication of a held-out validation split. Because η directly controls how far synthetic anomalies deviate from normal, selecting it on the test set can inflate the reported AUROC. Please report how η was chosen, provide validation curves with error bars, and clarify whether the reported test numbers correspond to a single selected η or to an oracle selection.
  3. [Tables 1–4] All experiments are repeated 10 times but only averages are reported. Given the small margins over GLASS-h (e.g., ChestXray8 K=2: 72.23 vs 70.03; BraTS K=8: 87.84 vs 87.19), standard deviations and significance tests are needed to support the claim that PathoSCOPE is state of the art. Without variance information, the reported improvements may not be statistically meaningful.
  4. [Eq. (1)] As written, Eq. (1) divides by Σ Mp(h,w); for non-pathological embeddings ν_n the anomaly mask Mp is all zeros, so dlocal(ν_n) is undefined. The mask used for normal embeddings needs to be specified; otherwise the Local Contrastive Loss in Eq. (5) cannot be computed as described.
  5. [Section 2.2 and Conclusion] The authors acknowledge in the Conclusion that synthetic local pathologies 'lack explicit anatomical coherence,' which directly limits their resemblance to real localized pathologies. Since PiEG trains the discriminator against synthetic embeddings generated from the discriminator's own gradients, there is a risk that the model learns synthesis artifacts rather than real disease. The t-SNE evidence in Fig. 2 is qualitative only. Please add quantitative evidence that the synthetic-to-real transfer works, for example by ablating PiEG/GLCL and measuring performance on real pathologies, or by testing cross-synthesis robustness.
minor comments (5)
  1. [Fig. 5 caption] The caption contains a typo: 'ChestXrat8' should be 'ChestXray8'.
  2. [Section 2.4] In the sentence introducing Eq. (6), 'PGEs' should be 'GPEs' (Global Pathological Embeddings).
  3. [Conclusion] The word 'auxillary' should be 'auxiliary'.
  4. [Section 2.2] The coreset anchor budget is not specified; please state how many prototypes are retained in ΦA.
  5. [Section 2.1] The threshold τ used to classify an image as pathological is mentioned but never defined; please specify how it is set, especially for DICE computation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the adversarial GPE loop is standard and evaluation is on real pathology.

full rationale

Walking the chain: the prototypical anchor bank is extracted from non-pathological training images (Sec 2.2); GLCL losses pull/push embeddings relative to those anchors; PiEG generates GPEs by gradient ascent on Lglobal (Eq. 3), and the discriminator is trained with Lglobal (Eq. 6). This is an adversarial self-training loop, but it is not a definitional circularity: the central claim is the measured AUROC on real BraTS2020/ChestXray8 images, which are not used in Eq. 3 or in defining the synthetic GPEs. Test evaluation is external to the training loop. The paper has no load-bearing self-citation: the closest prior work, GLASS [5], is by different authors and is used as a baseline, not to justify the method's validity. The Tritanh 'unique solution' citation is also external. The only tuning concern is Section 3.4's eta selection, which is not described with a separate validation split; that is an evaluation-protocol question (potential test-set overfitting) rather than a circular derivation, because the reported AUROC is not equal by construction to the eta selection criterion. The Conclusion's admission that synthetic local pathologies 'lack explicit anatomical coherence' is a generalizability limitation, not evidence that the derivation is circular. Therefore no circular step is established.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The method depends on several hand-tuned hyperparameters and on unverified domain assumptions: ImageNet feature transfer, DTD texture patches as stand-ins for real disease, gradient-ascent synthetic embeddings as proxies for real pathology, and the interpretation of BraTS2020 slices as healthy. No new physical entities are introduced.

free parameters (6)
  • eta (perturbation strength) = 0.01 (BraTS2020), 0.05 (ChestXray8)
    Selected per dataset by sweeping values and reporting peak AUROC (Section 3.4, Fig. 5).
  • lambda0, lambda1 (Tritanh scaling factors) = not reported
    Hand-chosen scaling factors for pull and push forces in Eq. 2; no sensitivity analysis.
  • epsilon (Tritanh regularizer) = not reported
    Regularizing term in Eq. 2; value not given.
  • tau (image classification threshold) = not reported
    Threshold used to classify an image as pathological at inference; how it is set is not specified (Section 2.1).
  • T and mu (GPE gradient steps and noise mean) = T=20, mu=0.015
    Hand-chosen for PiEG initialization and optimization (Section 2.3).
  • coreset anchor budget = not reported
    Number of prototypical embeddings retained by coreset sampling affects all anchor distances but is not reported (Section 2.2).
assumptions (5)
  • domain assumption ImageNet-pretrained ResNet18 features transfer to medical images (brain MRI, chest X-ray) well enough for anomaly detection.
    Assumed in Section 2.2 where the pretrained extractor is used directly on medical images.
  • domain assumption DTD texture patches overlaid on healthy images produce a useful proxy for real local pathologies.
    Used for local anomaly generation in Section 2.2; the authors later note this lacks anatomical coherence (Conclusion).
  • ad hoc to paper Gradient ascent on the global loss yields synthetic global embeddings that resemble real pathological embeddings.
    Core to PiEG in Section 2.3; validated only qualitatively via t-SNE, not quantitatively.
  • domain assumption The 'healthy' BraTS2020 slices are representative of genuinely healthy anatomy.
    The paper does not describe how healthy samples are obtained from a tumor segmentation dataset (Section 3.1).
  • domain assumption A coreset anchor bank built from 2-8 healthy samples captures the anatomical variance needed for reliable distances.
    Central to GLCL's pull and push objectives; no analysis of coverage with few shots (Section 2.2).

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Cite this review

Pith. "Pith review of PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings." pith.science (2026). https://pith.science/paper/XDRBX4L3

@misc{pith2026250517614,
  author       = {Pith},
  title        = {Pith review of: PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XDRBX4L3}},
  note         = {Machine review of arXiv:2505.17614}
}
read the original abstract

Unsupervised pathology detection trains models on non-pathological data to flag deviations as pathologies, offering strong generalizability for identifying novel diseases and avoiding costly annotations. However, building reliable normality models requires vast healthy datasets, as hospitals' data is inherently biased toward symptomatic populations, while privacy regulations hinder the assembly of representative healthy cohorts. To address this limitation, we propose PathoSCOPE, a few-shot unsupervised pathology detection framework that requires only a small set of non-pathological samples (minimum 2 shots), significantly improving data efficiency. We introduce Global-Local Contrastive Loss (GLCL), comprised of a Local Contrastive Loss to reduce the variability of non-pathological embeddings and a Global Contrastive Loss to enhance the discrimination of pathological regions. We also propose a Pathology-informed Embedding Generation (PiEG) module that synthesizes pathological embeddings guided by the global loss, better exploiting the limited non-pathological samples. Evaluated on the BraTS2020 and ChestXray8 datasets, PathoSCOPE achieves state-of-the-art performance among unsupervised methods while maintaining computational efficiency (2.48 GFLOPs, 166 FPS).

Figures

Figures reproduced from arXiv: 2505.17614 by the authors.

Figure 1
Figure 1. Overall architecture of the PathoSCOPE – We propose Pathology-informed Embedding Generation (PiEG) module, effec￾tively generate pathological embeddings that mimic real-world pathology patterns. – We achieve state-of-the-art performance in few-shot UPD on BraTS2020 and ChestXray8 datasets while maintaining high throughput and low GFLOPs, paving the way for clinical deployment. 2 Method 2.1 Overview The overview of P… view at source ↗
Figure 2
Figure 2. Comparison of t-SNE for synthetic embeddings on ChestXray8 dataset. The [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Visualization of BraTS2020 Heatmap Prediction [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Comparison of t-SNE for (a) GLASS-Hypersphere, (b) GLASS-Manifold, and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Evaluation of the perturbation strength ( [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Works this paper leans on

31 extracted references · 25 canonical work pages

  1. [1]

    In: Asian Conference on Computer Vision

    Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: Semi-supervised anomaly detection via adversarial training. In: Asian Conference on Computer Vision. pp. 622–637. Springer (2019)

    work page 2019

  2. [2]

    Scientific data 4(1), 1–13 (2017)

    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1–13 (2017)

    2017

  3. [3]

    arXiv preprint arXiv:1811.02629 (2018)

    Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

    arXiv 2018

  4. [4]

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

    Bao, J., Sun, H., Deng, H., He, Y ., Zhang, Z., Li, X.: Bmad: Benchmarks for medical anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4042–4053 (2024)

    work page 2024

  5. [5]

    arXiv preprint arXiv:2407.09359 (2024)

    Chen, Q., Luo, H., Lv, C., Zhang, Z.: A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization. arXiv preprint arXiv:2407.09359 (2024)

    arXiv 2024

  6. [6]

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

    Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3606–3613 (2014)

    2014

  7. [7]

    Eye 38(12), 2448–2456 (2024)

    Colman, B.D., Zhu, Z., Qi, Z., van der Walt, A.: From real world data to real world evidence to improve outcomes in neuro-ophthalmology. Eye 38(12), 2448–2456 (2024)

    work page 2024

  8. [8]

    In: International Conference on Pattern Recognition

    Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition. pp. 475–489. Springer (2021)

    work page 2021

Show all 31 references
  1. [9]

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

    Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9737–9746 (2022)

    2022

  2. [10]

    IEEE Access 11, 17906–17921 (2023)

    Esmaeili, M., Toosi, A., Roshanpoor, A., Changizi, V ., Ghazisaeedi, M., Rahmim, A., Sabokrou, M.: Generative adversarial networks for anomaly detection in biomedical imaging: A study on seven medical image datasets. IEEE Access 11, 17906–17921 (2023)

    2023

  3. [11]

    Computers in Biology and Medicine 152, 106391 (2023)

    Garcea, F., Serra, A., Lamberti, F., Morra, L.: Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine 152, 106391 (2023)

    2023

  4. [12]

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

    Gudovskiy, D., Ishizaka, S., Kozuka, K.: Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 98–107 (2022)

    2022

  5. [13]

    BMC bioinformatics 22, 1–20 (2021)

    Han, C., Rundo, L., Murao, K., Noguchi, T., Shimahara, Y ., Milacski, Z.Á., Koshino, S., Sala, E., Nakayama, H., Satoh, S.: Madgan: Unsupervised medical anomaly detection gan using multiple adjacent brain mri slice reconstruction. BMC bioinformatics 22, 1–20 (2021)

    2021

  6. [14]

    In: European Conference on Computer Vision

    Huang, C., Guan, H., Jiang, A., Zhang, Y ., Spratling, M., Wang, Y .F.: Registration based few-shot anomaly detection. In: European Conference on Computer Vision. pp. 303–319. Springer (2022)

    2022

  7. [15]

    IEEE Transactions on Medical Imaging 43(1), 241–252 (2024)

    Lagogiannis, I., Meissen, F., Kaissis, G., Rueckert, D.: Unsupervised pathology detection: A deep dive into the state of the art. IEEE Transactions on Medical Imaging 43(1), 241–252 (2024). https://doi.org/10.1109/TMI.2023.3298093 10 Chin et al

    2024

  8. [16]

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

    Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R.J., Camps, O.: Towards visually explaining variational autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8642–8651 (2020)

    2020

  9. [17]

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

    Liu, Z., Zhou, Y ., Xu, Y ., Wang, Z.: Simplenet: A simple network for image anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20402–20411 (2023)

    2023

  10. [18]

    In: International MICCAI Brainlesion Workshop

    Meissen, F., Paetzold, J., Kaissis, G., Rueckert, D.: Unsupervised anomaly localization with structural feature-autoencoders. In: International MICCAI Brainlesion Workshop. pp. 14–24. Springer (2022)

    2022

  11. [19]

    IEEE transactions on medical imaging 34(10), 1993–2024 (2014)

    Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y ., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10), 1993–2024 (2014)

    2014

  12. [20]

    Orphanet Journal of Rare Diseases 19(1), 405 (2024)

    de Oliveira, B.M., Bernardi, F.A., Baiochi, J.F., Neiva, M.B., Artifon, M., Vergara, A.A., Martins, A.M., Grumach, A.S., Acosta, A.X., Husny, A.S.E., et al.: Epidemiological char- acterization of rare diseases in brazil: A retrospective study of the brazilian rare diseases net...

    2024

  13. [21]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Reiss, T., Hoshen, Y .: Mean-shifted contrastive loss for anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 2155–2162 (2023)

    2023

  14. [22]

    Medical image analysis 54, 30–44 (2019)

    Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis 54, 30–44 (2019)

    2019

  15. [23]

    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

  16. [24]

    Medical Image Analysis 80, 102526 (2022)

    Silva-Rodríguez, J., Naranjo, V ., Dolz, J.: Constrained unsupervised anomaly segmentation. Medical Image Analysis 80, 102526 (2022)

    2022

  17. [25]

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

    Wang, X., Peng, Y ., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Patter...

    2017

  18. [26]

    Nature 634(8035), 970–978 (2024)

    Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y ., et al.: A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634(8035), 970–978 (2024)

    2024

  19. [27]

    Neurocomputing 493, 497–535 (2022)

    Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., Ding, N.: Gan-based anomaly detection: A review. Neurocomputing 493, 497–535 (2022)

    2022

  20. [28]

    arXiv preprint arXiv:2012.11113 (2020)

    Yang, Y ., Xiang, S., Zhang, R.: Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders. arXiv preprint arXiv:2012.11113 (2020)

    2020 arXiv

  21. [29]

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

    Zavrtanik, V ., Kristan, M., Skoˇcaj, D.: DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8330–8339 (2021)

    2021

  22. [30]

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

    Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V ., Dalca, A.V .: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8543–8553 (2019)

    2019

  23. [31]

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

    Zhao, Y .: Omnial: A unified cnn framework for unsupervised anomaly localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3924–3933 (2023)

    2023

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Reviewed August 7, 2026 · model on record in the stance chip above.