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REVIEW 4 major objections 4 minor 38 references

Self-Attentive Adversarial Stain Normalization

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A self-attentive adversarial network maps H&E stains from many labs to one target domain while preserving biopsy structure better than existing stain normalizers.

desk verdict A sensible GAN architecture for stain normalization, but the quantitative claim rests on a circular SSIM metric and is not supported. read the letter →

arxiv 1909.01963 v3 pith:PP3JHXZK submitted 2019-09-04 eess.IV cs.CVq-bio.QM

Aman Shrivastava , Will Adorno , Yash Sharma , Lubaina Ehsan , S. Asad Ali , Sean R. Moore , Beatrice C. Amadi , Paul Kelly
show 2 more authors
Sana Syed Donald E. Brown
This is my paper · ORCID
classification eess.IVcs.CVq-bio.QM
keywords stainnormalizationadversariallearningself-attentionhistopathologyH&Estainingimage-to-imagetranslationstructuralsimilaritywholeslideimages
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

Histopathology slides stained with H&E look different depending on which lab prepared them, and those color differences can bias both human readers and deep-learning models. This paper proposes SAASN, an unsupervised generative adversarial network that translates images from several stain domains into one common target stain domain, so that slides from different sites become comparable. The authors claim that adding self-attention layers and a structural cycle-consistency loss based on the structural similarity index preserves fine cellular structure during translation, and that SAASN consistently beats popular alternatives on duodenal biopsy patches and a public mitosis dataset. If correct, the method would let multi-site biopsy data be pooled and analyzed without retraining or manual target-image selection.

What carries the argument

The load-bearing mechanism is the loss function in Eq. (8): $L = L^Y_{adv} + L^X_{adv} + \alpha L_{cyc} + \beta L_{scyc} + \gamma L_{dssim} + \delta L_{id}$, with self-attention layers inserted after every convolutional block of a U-Net generator and a CNN discriminator. The structural cycle-consistency loss $L_{scyc}$ and the direct structural dissimilarity loss $L_{dssim}$ both use $1-\mathrm{SSIM}(a,b)$ over grayscale windows, which measures whether tissue architecture is preserved while ignoring stain color; that is what makes the many-to-one mapping feasible, because the inverse generator $G_{XY}$ cannot return an image to the original sub-domain and so the usual L1 cycle loss alone would fail. Self-attention, computed via query/key/value transforms with a learnable scale $\mu$ initialized to zero, lets each output location attend to the whole feature map, a mechanism the paper credits for preserving fine-grain structures such as granules and cell boundaries. Spectral normalization stabilizes training.

What would settle it

Replace the learned generator with the identity function on the same test set and compute the same gray-scale SSIM against the originals; if identity also scores about 0.98-0.99, then the metric cannot distinguish SAASN from doing nothing.

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Extended reading notes

Core claim

SAASN is a many-to-one unsupervised image-to-image translation model. It learns a generator $G_{YX}: X \to Y$ from a composite source domain $X$ (made of several stain sub-domains $X^{(k)}$) to a target domain $Y$, together with an inverse $G_{XY}$, using adversarial losses, a standard cycle-consistency loss, and three structural regularizers: a structural cycle-consistency loss $L_{\mathrm{scyc}} = (1-\mathrm{SSIM}(G_{XY}(G_{YX}(x)),x))/2$, a direct structural dissimilarity loss between mapped and original images, and an identity loss. Because SSIM is computed on gray-scale images, these losses are color-agnostic and therefore suitable for many-to-one mapping, where a cycled image cannot be expected to match the input's color. Self-attention blocks in both generator and discriminator model long-range spatial dependencies, which the paper argues is what lets the generator synthesize fine details such as eosinophilic granules in Paneth cells and keep red blood cells distinct. On duodenal biopsies from three sites and on the MITOS-ATYPIA scanner-pair data, SAASN reports higher mean SSIM and much lower variance than Vahadane, Macenko, and StainGAN (e.g., $0.977 \pm 0.007$ versus $0.942 \pm 0.033$ for $X^{(1)}\to Y$), and a blinded pathologist review confirmed that medically relevant cell types were preserved.

Load-bearing premise

The load-bearing assumption is that the structural-similarity score between a normalized image and the original image measures successful stain normalization, but a no-change copy would also score near 1.0, so the reported scores alone cannot prove that stain transfer occurred.

Editorial extensions

If this is right

  • One trained SAASN model can normalize patches from any number of source stain sub-domains into a single target domain, so a multi-site cohort can be brought to a common appearance without learning separate one-to-one mappings.
  • Because the same frozen weights process every patch, full whole-slide images can be normalized patch-by-patch and stitched without the patch-boundary inconsistency that plagues per-patch traditional normalization.
  • The SSIM-based structural losses are color-agnostic, so the framework should transfer to any many-to-one image translation problem where content structure, not color, is the invariant; the authors make this claim explicitly for non-medical image translation.
  • Blinded review by a board-certified pathologist found that cell types such as neutrophils, eosinophils, goblet cells, Paneth cells, and neuroendocrine cells survive SAASN normalization, meaning downstream diagnostic models can be trained on normalized data without losing diagnostic features.

Reading between the lines

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

  • A testable extension the paper does not run: measure a stain-color distribution distance (e.g., histogram or mean color in H&E optical-density space) between normalized outputs and target-domain images; SSIM alone cannot confirm that the target stain color was reached.
  • The identity loss and the SSIM-based structural loss both reward minimal change, so the method may be biased toward conservative transformations; on datasets where source and target stains strongly overlap, this bias could explain high SSIM without full stain transfer, and would be worth testing on images with extreme stain differences.
  • The same structural-cycle-consistency trick could be applied to other modality-shift problems such as MRI field-strength harmonization or cross-scanner CT, where color or contrast changes are superficial and anatomy must be preserved.
  • Because self-attention is quadratic in spatial resolution, applying SAASN to full gigapixel WSIs would likely need a linear-attention or windowed variant; a patch-based pipeline already works, but a full-slide variant is the natural next scaling test.
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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

4 major / 4 minor

Summary. The paper proposes SAASN, a CycleGAN-style unsupervised many-to-one stain normalization framework for H&E histopathology images. The method combines self-attention in both generator and discriminator, a structural cycle-consistency loss and a structural dissimilarity loss based on SSIM, an identity loss, and a discriminator boundary control factor intended to handle multiple stain sub-domains mapping to a common target domain. The authors compare SAASN with StainGAN, Macenko, and Vahadane on a duodenal biopsy dataset and on the MITOS-ATYPIA dataset, reporting SSIM values between normalized and original images, together with a qualitative pathologist review of whole-slide normalizations.

Significance. The problem addressed is practically important: stain variation across sites can bias both human diagnosis and deep-learning models, and an effective many-to-one normalization method would be valuable. The paper also has genuine strengths: it formulates the many-to-one setting explicitly, integrates self-attention in a reasonable way, performs an ablation study, and includes a pathologist review of structural preservation. However, the central quantitative claim is not supported by the reported evaluation. The SSIM metric used in Tables 1 and 2 is computed between the normalized output and the original input, is explicitly gray-scale based, and is directly optimized by Eqs. (4) and (5). Consequently, the high SSIM values are substantially a measurement of how well the network minimizes its own training objective, and an identity transform would also score near 1.0. No quantitative evidence shows that the stain appearance was actually transferred to the target domain. The significance of the proposed method can therefore only be assessed once a valid evaluation protocol is provided.

major comments (4)
  1. [Section 5, Tables 1 and 2] The primary quantitative metric, SSIM between the normalized image and the original source image, cannot measure whether stain normalization occurred. This metric is directly optimized by the structural cycle-consistency loss in Eq. (4) and the structural dissimilarity loss in Eq. (5). An identity mapping would achieve SSIM close to 1 without performing any stain transfer, so the near-0.98 values reported for SAASN are indistinguishable from 'doing nothing' under this protocol.
  2. [Section 5, first paragraph and Eq. (6)] SSIM is computed on gray-scale images, as the text explicitly states. This makes the metric color-agnostic, so it ignores the very transformation that defines the stain normalization task. A method that only changes color and a method that changes nothing would receive essentially the same SSIM score, meaning Tables 1 and 2 do not provide evidence about stain transfer.
  3. [Section 5, pathologist review paragraph] The blind review by the pathologist confirms preservation of cell types and tissue structure, which is relevant to the structural-preservation sub-claim. However, the review is not reported to have assessed whether the stain appearance was successfully transferred to the target domain. Thus it does not support the stain-normalization sub-claim, which is the paper's headline contribution.
  4. [Section 4.1 and Section 5] No quantitative comparison between normalized images and the target domain is provided; there is no color histogram or stain-vector comparison, no FID or related distributional metric, and no downstream task such as training on target-domain images and testing on normalized images. Without such evidence, the abstract's assertion of 'consistent and superior performance' is unsupported.
minor comments (4)
  1. [Eq. (11)] The attention weight formula uses g(x_j), but g is not defined in the preceding equations; from Eq. (10) it should presumably be q(x_j) or v(x_j), and this should be corrected to match the SAGAN formulation.
  2. [Section 4.2] The sentence beginning 'In previous encoder-decoder architectures [20,31,35].' is an incomplete fragment and should either be completed or removed.
  3. [Section 5] The paragraph describing the pathologist review is repeated verbatim and ends with a garbled string; this needs to be cleaned up.
  4. [Figure 3 caption] The phrase 'The target image only applies to the Macenko and Vahadane techniques' is ambiguous and should be reworded to clarify that these two methods use a single target image, while StainGAN and SAASN use the target domain distribution.

Circularity Check

1 steps flagged · score 6.0 of 10

The reported SSIM evaluation metric is the same quantity SAASN is trained to maximize (Eq. 5), so Tables 1–2 are an in-sample score, not an independent comparison.

  1. fitted input called prediction [Section 3 Eq. (5) and Eq. (8); Section 5 Results and Evaluation, Tables 1–2.]
    "To evaluate the stain transfer, the Structural Similarity (SSIM) index is again utilized. SSIM is calculated by comparing the normalized image with the original. Both images are converted to gray-scale before beginning SSIM calculations. ... the structural dissimilarity loss is also computed between the mapped and the original image: Ldssim = (1−SSIM (GY X (x),x))/2 + (1−SSIM (GXY (y),y))/2 ... The parameter values of α = 10, β = 10, γ = 10 and δ = 0.1 were empirically chosen after experimentation for the evaluation model."

    Eq. (8) minimizes the total loss with weight γ=10 on Ldssim, and Eq. (5) defines Ldssim as (1−SSIM)/2 between the mapped image and the original input, e.g., GY X(x) versus x. The evaluation in Section 5 and Tables 1–2 reports exactly SSIM(GY X(x), x) as the performance metric. Thus the high SAASN scores are values of the training objective being optimized, not an independent measure of stain normalization success. Because SSIM is computed on gray-scale images, this metric is insensitive to the stain-color transfer that defines the task; an identity output would obtain SSIM ≈ 1 by definition. Compared with Macenko, Vahadane, and StainGAN, none of which optimize this SSIM objective, SAASN is being scored on a metric it was explicitly trained to maximize.

full rationale

The central quantitative claim of 'consistent and superior performance' rests on Tables 1 and 2, whose entries are SSIM values between the normalized image and the original. That scalar is the exact term minimized by Eq. (5) with γ=10 in Eq. (8), so the comparison is partially circular: SAASN is trained to maximize the same index it is evaluated on, while the comparison methods do not optimize this index. This does not invalidate all evidence: qualitative figures, the WSI stitching demonstration, and the pathologist's blind review provide independent, albeit limited, support for structure preservation. However, the tabulated SSIM advantage is not an independent confirmation and cannot by itself establish that stain appearance was transferred, since it is computed on gray-scale images. No load-bearing self-citation was found: citation [25] is a standard image-buffer training detail, and the CycleGAN/SAGAN references are external prior art. The score is 6 because the main quantitative evidence reduces to a training objective, but the paper retains independent visual and expert-review components that keep it from being entirely circular.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim depends on hand-tuned loss weights and the assumption that SSIM between the normalized and original image measures successful stain normalization. The domain-transfer framing and the effectiveness of the boundary control factor are also assumed rather than derived. No new physical or biological entities are introduced.

free parameters (5)
  • loss weight α for cycle consistency = 10
    Empirically chosen after experimentation (Section 4.3).
  • loss weight β for structural cycle consistency = 10
    Empirically chosen (Section 4.3).
  • loss weight γ for structural dissimilarity = 10
    Empirically chosen (Section 4.3).
  • loss weight δ for identity loss = 0.1
    Empirically chosen (Section 4.3).
  • attention dimension reduction factor = C/8
    Set as C/8 in Section 4.2; an adjustable architectural choice, not derived from first principles.
assumptions (4)
  • domain assumption SSIM is a valid proxy for both structural preservation and stain normalization quality.
    The paper uses SSIM as both a loss term and the sole quantitative evaluation metric. SSIM measures gray-scale structural similarity between input and output, not whether the stain color distribution matches the target domain.
  • domain assumption Stain variation across sites can be modeled as an image-to-image domain translation problem.
    The entire approach frames stain variation as a domain shift learnable by a GAN. This is a common assumption in the field but is not independently verified.
  • domain assumption Cycle consistency with SSIM-based losses preserves diagnostically relevant structures.
    The structural cycle-consistency loss (Eq. 4) is assumed to keep biological structures intact, but no direct validation of clinical utility is provided.
  • ad hoc to paper The discriminator boundary control factor prevents unwanted overlap of sub-domains and the target domain.
    The extra term added to Eq. 2 is introduced heuristically to avoid the decision boundary of D_X including parts of Y. No theoretical or empirical justification beyond anecdotal ablation is given.

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Cite this review

Pith. "Pith review of Self-Attentive Adversarial Stain Normalization." pith.science (2026). https://pith.science/paper/PP3JHXZK

@misc{pith2026190901963,
  author       = {Pith},
  title        = {Pith review of: Self-Attentive Adversarial Stain Normalization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PP3JHXZK}},
  note         = {Machine review of arXiv:1909.01963}
}
read the original abstract

Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. To reduce this bias, slides need to be translated to a common domain of stain appearance before analysis. We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.

Figures

Figures reproduced from arXiv: 1909.01963 by the authors.

Figure 1
Figure 1. (Left) H&E stained duodenal biopsy patches created from whole slide images sourced from different locations. (Right) Visual example of a many-to-one stain trans￾fer network. Two different stains are present as inputs within X: X (1) and X (2). Both of these domains are translated to Y with GXY . To complete the cycle, GY X returns the image back to the X domain, but it can no longer be mapped directly to the input s… view at source ↗
Figure 2
Figure 2. Left: Results when mapping was done from two sub-domains of X to Y . Patches from both domains X (1) and X (2) are translated to domain Y using GY X. These generated images are then translated back to a new domain defined by a GXY as a combination of stain distributions of sub-domains of X. Patches on either end of the second column are real images from domain Y and have been added to visually show the performance o… view at source ↗
Figure 3
Figure 3. Visual comparison of performance in cases where Macenko and Vahadane tech￾niques struggle to properly transfer stain in each scenario. The target image only applies to the Macenko and Vahadane techniques. we used the Adam optimizer [12] with a learning rate of 0.0002 and batch size 16. The model was trained for the first 50 epochs with a fixed learning rate and the next 50 epochs while linearly decaying the learning… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Visual and quantitative comparison of performance between StainGAN and ablation study on SAASN. The numbers indicate the overall mean ± standard deviation of the SSIM index for the transformation. All models were trained in a many-to-one setup. To evaluate the stain tr…
Figure 5
Figure 5. Figure 5: Normalized Whole Slide Image using ours and traditional approaches. Macenko was chosen because it performed better than Vahadane on our dataset. The target slide for Macenko was empirically selected to give the best translation. combined Macenko and Vahadane SSIM value…
Figure 6
Figure 6. Figure 6: Visual comparison of performance in cases where Macenko and Vahadane tech￾niques perform very well according to a combined SSIM index. The target image only applies to the Macenko and Vahadane techniques. The main results section included a visual comparison of SAASN s…
Figure 7
Figure 7. Figure 7: One-to-one (K=1) model. Left: Translation from domain X (1) to Y and back to domain X (1) . Right: Translation from domain Y to X (1) and back to Y [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: One-to-one (K=1) model. Left: Translation from domain X (2) to Y and back to domain X (2) . Right: Translation from domain Y to X (2) and back to Y [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Many-to-one (K=2) model. Left: Translation from domain X to Y and back to domain Xˆ. Right: Translation from domain Y to Xˆ and back to Y [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: The model was also trained on Yosemite summer to winter dataset from the CycleGAN paper. Left: Translation from winter to summer and back to winter. Right: Translation from summer to winter and back to summer. The model was trained with the same parameters as for the …

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Works this paper leans on

38 extracted references · 24 canonical work pages

  1. [1]

    In: Medical Imaging 2014: Digital Pathology

    Bejnordi, B.E., Timofeeva, N., Otte-H¨ oller, I., Karssemeijer, N., van der Laak, J.A.: Quantitative analysis of stain variability in histology slides and an algorithm for standardization. In: Medical Imaging 2014: Digital Pathology. vol. 9041, p. 904108. International Society for Optics and Photonics (2014)

    work page 2014

  2. [2]

    IEEE transactions on medical imaging 37(3), 792–802 (2017)

    BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE transactions on medical imaging 37(3), 792–802 (2017)

    work page 2017

  3. [3]

    IEEE Trans- actions on Image Processing 15(5), 1143–1152 (2006)

    Coltuc, D., Bolon, P., Chassery, J.M.: Exact histogram specification. IEEE Trans- actions on Image Processing 15(5), 1143–1152 (2006)

    work page 2006

  4. [4]

    Fischer, A.H., Jacobson, K.A., Rose, J., Zeller, R.: Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harbor Protocols 2008(5), pdb–prot4986 (2008) 6 Please refer to https://github.com/4m4n5/saasn-stain-normalization and additional materials for implementation and additional results. 14 Shrivastava et al

    work page 2008

  5. [5]

    In: Advances in neural information processing systems

    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)

    2014

  6. [6]

    science 313(5786), 504–507 (2006)

    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neu- ral networks. science 313(5786), 504–507 (2006)

    work page 2006

  7. [7]

    arXiv preprint arXiv:1502.03167 (2015)

    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

    arXiv 2015

  8. [8]

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

    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with condi- tional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)

    2017

Show all 38 references
  1. [9]

    Computerized Medical Imaging and Graphics 57, 50–61 (2017)

    Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse autoencoders (stanosa): Application to digital pathology. Computerized Medical Imaging and Graphics 57, 50–61 (2017)

    2017

  2. [10]

    IEEE Transactions on Biomedical Engineering 61(6), 1729–1738 (2014)

    Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Transactions on Biomedical Engineering 61(6), 1729–1738 (2014)

    2014

  3. [11]

    In: Proceedings of the 34th Inter- national Conference on Machine Learning-Volume 70

    Kim, T., Cha, M., Kim, H., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th Inter- national Conference on Machine Learning-Volume 70. pp. 1857–1865. JMLR. org (2017)

    2017

  4. [12]

    arXiv preprint arXiv:1412.6980 (2014)

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

    2014 arXiv

  5. [13]

    Medical image analysis 42, 60–88 (2017)

    Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., S´ anchez, C.I.: A survey on deep learning in medical image analysis. Medical image analysis 42, 60–88 (2017)

    2017

  6. [14]

    In: Advances in neural information processing systems

    Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation net- works. In: Advances in neural information processing systems. pp. 700–708 (2017)

    2017

  7. [15]

    Liu, Y., Gadepalli, K.K., Norouzi, M., Dahl, G., Kohlberger, T., Venugopalan, S., Boyko, A.S., Timofeev, A., Nelson, P.Q., Corrado, G., Hipp, J., Peng, L., Stumpe, M.: Detecting cancer metastases on gigapixel pathology images. Tech. rep., arXiv (2017), https://arxiv.org/abs/1703.02442

    2017 arXiv

  8. [16]

    In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

    Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quan- titative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. pp. 1107–1110. IE...

    2009

  9. [17]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares gen- erative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2794–2802 (2017)

    2017

  10. [18]

    arXiv preprint arXiv:1802.05957 (2018)

    Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)

    2018 arXiv

  11. [19]

    arXiv preprint arXiv:1606.01933 (2016)

    Parikh, A.P., T¨ ackstr¨ om, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)

    2016 arXiv

  12. [20]

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

    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context en- coders: Feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2536–2544 (2016)

    2016

  13. [21]

    IEEE Computer graphics and applications 21(5), 34–41 (2001)

    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between im- ages. IEEE Computer graphics and applications 21(5), 34–41 (2001)

    2001

  14. [22]

    In: International Conference on Medical image computing and computer-assisted intervention

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedi- cal image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015) Self-Attentive Adversarial Stain Normalization 15

    2015

  15. [23]

    Micron 114, 42–61 (2018)

    Roy, S., kumar Jain, A., Lal, S., Kini, J.: A study about color normalization meth- ods for histopathology images. Micron 114, 42–61 (2018)

    2018

  16. [24]

    In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

    Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: Staingan: Stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). pp. 953–956. IEEE (2019)

    2019

  17. [25]

    In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition

    Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learn- ing from simulated and unsupervised images through adversarial training. In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition. pp. 2107–2116 (2017)

    2017

  18. [26]

    arXiv preprint arXiv:1611.02200 (2016)

    Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200 (2016)

    2016 arXiv

  19. [27]

    arXiv preprint arXiv:1607.08022 (2016)

    Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing in- gredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)

    2016 arXiv

  20. [28]

    IEEE transactions on medical imaging 35(8), 1962–1971 (2016)

    Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang, L., Baust, M., Steiger, K., Schlitter, A.M., Esposito, I., Navab, N.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE transactions on medical imaging 35(8), 1962–1971 (2016)

    2016

  21. [29]

    In: Advances in neural information processing systems

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)

    2017

  22. [30]

    In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7794–7803 (2018)

    2018

  23. [31]

    In: European Conference on Computer Vision

    Wang, X., Gupta, A.: Generative image modeling using style and structure ad- versarial networks. In: European Conference on Computer Vision. pp. 318–335. Springer (2016)

    2016

  24. [32]

    IEEE transactions on image processing 13(4), 600–612 (2004)

    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality as- sessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004)

    2004

  25. [33]

    Journal of pathology informatics 10 (2019)

    Wei, J.W., Wei, J.W., Jackson, C.R., Ren, B., Suriawinata, A.A., Hassanpour, S.: Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach. Journal of pathology informatics 10 (2019)

    2019

  26. [34]

    In: Proceedings of the IEEE international conference on computer vision

    Yi, Z., Zhang, H., Tan, P., Gong, M.: Dualgan: Unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE international conference on computer vision. pp. 2849–2857 (2017)

    2017

  27. [35]

    In: European Conference on Computer Vision

    Yoo, D., Kim, N., Park, S., Paek, A.S., Kweon, I.S.: Pixel-level domain transfer. In: European Conference on Computer Vision. pp. 517–532. Springer (2016)

    2016

  28. [36]

    In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A., de With, P.H.: Stain normalization of histopathology images using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 573–577. IEEE (2018)

    2018

  29. [37]

    arXiv preprint arXiv:1805.08318 (2018)

    Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative ad- versarial networks. arXiv preprint arXiv:1805.08318 (2018)

    2018 arXiv

  30. [38]

    In: Proceedings of the IEEE interna- tional conference on computer vision

    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE interna- tional conference on computer vision. pp. 2223–2232 (2017) 16 Shrivastava et al. A Conclusions The proposed framew...

    2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.