Stain-Invariant Representation for Tissue Classification in Histology Images

Manahil Raza; Nasir Rajpoot; Saad Bashir; Talha Qaiser

REVIEW 4 major objections 5 minor 25 references

Stain-Invariant Representation for Tissue Classification in Histology Images

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A simple stain-augmentation and consistency-loss scheme reaches 87.8% accuracy on cross-domain tissue classification, beating the ImageNet baseline by 22 points and the state-of-the-art IMPaSh method by 1 point.

desk verdict A plausible but under-ablated stain-consistency extension of CIRCLe to colorectal histology; the 1% gain over IMPaSh is unverified without error bars. read the letter →

arxiv 2411.15237 v1 pith:NZA2XI4T submitted 2024-11-21 cs.CV cs.AIcs.LG

Manahil Raza , Saad Bashir , Talha Qaiser , Nasir Rajpoot This is my paper · ORCID

classification cs.CVcs.AIcs.LG

keywords stainaugmentationdomaingeneralizationhistologyimageclassificationcolorectalcancertissuefeatureconsistencyregularizationmatrixperturbationcross-domaincomputationalpathology

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 tries to establish that a straightforward stain-augmentation strategy can make deep learning models invariant to the stain variations that cause domain shift in histology images. The proposed framework generates six stain-altered copies of each training image by perturbing the stain matrix extracted with Vahadane's method, and then trains a ResNet-18 classifier with an extra mean-squared-error loss that pulls feature representations of source and augmented images together. Evaluated on cross-domain multi-class tissue classification from K19 to K16 colorectal cancer images, the method reaches 87.8% accuracy, outperforming the ImageNet pre-trained baseline by 22 percentage points and the state-of-the-art IMPaSh method by 1 percentage point while requiring less computation. If the central claim holds, it would show that simple stain augmentation plus feature consistency is a viable and economical alternative to more complex domain-adaptation methods in computational pathology.

What carries the argument

The load-bearing machinery is stain matrix perturbation: for each image, Vahadane's sparse stain separation extracts a stain matrix, the stain concentrations are randomly perturbed (implemented via TIAToolbox), producing stain-altered versions of the same tissue. A shared ResNet-18 feature extractor processes both the source and augmented images, and a mean-squared-error loss forces their feature embeddings to be close. This consistency regularizer, combined with the cross-entropy classification loss, is what the paper credits for learning representations that are insensitive to stain variation and therefore to dataset shift.

What would settle it

Compute the actual stain-concentration ranges of K19 and K16 using Vahadane separation; if the perturbation range used for K19 does not include the K16 stain statistics, then the claimed transfer cannot be explained by stain invariance and the method's stated mechanism is falsified. Alternatively, an ablation that removes the MSE loss while keeping the same six stain augmentations should show a drop in accuracy; if it does not, the consistency loss is not the cause of the gain.

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Extended reading notes

Core claim

The paper claims that a straightforward combination of stain matrix perturbation and a feature-consistency loss yields stain-invariant representations that transfer across histology datasets. On K19-to-K16 cross-domain multi-class tissue classification, the method reaches 87.8% accuracy, exceeding the ImageNet pre-trained baseline (65.4%) by 22.4 percentage points and the contrastive-learning-based IMPaSh method (86.8%) by 1 point, while requiring fewer computational resources than IMPaSh. The method generates six stain-augmented images per input via Vahadane stain separation and perturbation of stain concentrations, then minimizes an MSE loss between the feature representations of source and augmented images, alongside the cross-entropy classification loss. The result supports the paper's thesis that stain augmentation alone, combined with a simple representation-consistency regularizer, can handle domain shift in computational pathology without target-domain data.

Load-bearing premise

The method assumes that perturbing stain concentrations in source-domain images with the Vahadane-derived range covers the stain variability of the unseen target domain, so the invariance learned from these augmentations actually transfers to the true K19-to-K16 domain shift; no direct measurement of that distributional overlap is given.

Editorial extensions

If this is right

Cross-domain tissue classification can improve without any target-domain images, because the method only requires stain augmentations of source images.
The feature-consistency loss makes a standard CNN backbone more transferable, likely benefiting downstream tasks that use these features.
The method is computationally cheaper than contrastive approaches such as IMPaSh, suggesting that stain augmentation plus a simple regularizer may be sufficient for some domain-generalization settings.
The paper raises the question of whether stain augmentation alone can replace more complex domain-adaptation methods in histology, pointing toward a simpler baseline for future work.

Reading between the lines

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

The reported 1-point improvement over IMPaSh may not be statistically robust; without repeated runs or confidence intervals, the practical advantage is uncertain.
The method's success depends on the perturbation range covering the target domain's stain variability; adjusting the perturbation magnitude to match target stain statistics when a few target samples are available could further improve transfer.
The same framework could be tested with other perturbation families, such as mixing stain matrices from multiple source hospitals or using adversarial stain generation, to see if even broader invariance helps.
Because the mechanism is tissue-agnostic, the approach should generalize to other histology classification tasks and other stain-sensitive image domains, though the paper only demonstrates colorectal cancer tissue classification.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper is a short MIUA-style report that transplants CIRCLe's color-invariant representation learning into histopathology. It generates six stained-augmented versions of each K19 tile via Vahadane stain-matrix perturbation and adds an MSE consistency loss between source and augmented feature maps. On K19-to-K16 colorectal tissue classification it reports 87.8% accuracy, beating IMPaSh by 1 point and the ImageNet baseline by 22 points. The direction is sensible, the writing is clear, and the authors cite the relevant prior work, including Tellez, Stain-AgLr, CIRCLe, and IMPaSh. They also end by asking whether stain augmentation alone is sufficient for domain generalization, which is a fair question rather than a claim.

The main weakness is that the central mechanism is not isolated. The reported gain over the lower bound could, in principle, come entirely from the stain augmentation itself—Tellez already showed that augmentation helps—while the consistency loss adds nothing. The paper never ablates the MSE loss against plain stain augmentation. That is the load-bearing missing experiment. The 1-point margin over IMPaSh is also unverified: there are no error bars or repeated seeds, and the table reports single runs. The comparison omits the two closest relatives (Stain-AgLr and CIRCLe), and there is no analysis of whether the perturbation range actually covers the stain variation in K16. On a single source-target pair, these gaps matter.

All of these are addressable. The method is simple, reproducible in principle, and the empirical claim, if true, is a modest but legitimate state-of-the-art on a standard benchmark. It is not a big leap, and the paper is honest about its scope.

I would send this to peer review if expanded into a full paper—it deserves a serious referee rather than a desk reject. The referee should ask for an ablation, error bars, and a comparison to stain-augmentation-only baselines. As is, it reads like an extended abstract; the missing ablation keeps it from being a strong contribution.

Who is this for? Someone working on stain robustness or domain generalization in computational pathology might want to read it and check the ablation claim, but I would not cite it in my own work. The novelty is borrowed from CIRCLe; the gain is small; the validation is thin.

Recommendation: engage with it as a workshop-level paper, but require the missing ablation before trusting the result.

Referee Report

4 major / 5 minor

Summary. The paper proposes a method for stain-invariant tissue classification in colorectal histology images, evaluated on cross-domain transfer from Kather-19 (K19) to Kather-16 (K16). The framework uses a ResNet-18 feature extractor with an MLP classifier and generates six stain-augmented versions of each input by perturbing Vahadane-estimated stain concentrations. A mean squared error loss enforces feature consistency between the source and augmented images, combined with a cross-entropy classification loss. On the K19-to-K16 seven-class task, the authors report 87.8% accuracy, outperforming a set of baselines including the ImageNet lower bound (65.4%), MoCoV2, InfoMin, and IMPaSh (86.8%), while claiming lower computational cost than IMPaSh.

Significance. If the reported result holds, the method would offer a simple and comparatively inexpensive alternative to contrastive pretraining for domain generalization in computational pathology. The strength of the paper is its use of an established external benchmark (K19 to K16) and comparison against several published baselines. The claim is plausible but under-supported: the advantage over IMPaSh is only one percentage point, no statistical reliability is reported, and no ablation isolates the contribution of the proposed consistency loss from stain augmentation alone. The paper ships no code and no reproducibility details beyond the method sketch, so the empirical claim cannot currently be verified. The significance is moderate: the method is incremental, but practically useful if the ablation and variance questions are resolved.

major comments (4)

[Table 1 / Methodology] The proposed contribution is the combination of six-fold Vahadane stain augmentation with an MSE feature-consistency loss, but Table 1 contains no ablation isolating these two components. In particular, there is no row for the model trained with stain augmentation alone and no row for the model trained with the consistency loss but no augmentation. Since stain augmentation alone is already known to be effective for histopathology classification (Tellez et al., [24]), the reported 0.878 accuracy could be entirely attributable to augmentation, and the claimed benefit of the regularizer would be unsupported. Please add at least the two ablations (augmentation-only and consistency-loss-only) under the same training schedule, and report the accuracy gap between them and the full method.
[Table 1] All results are reported from a single run without error bars, confidence intervals, or statistical significance tests. The proposed method's advantage over IMPaSh is 0.010 accuracy (0.878 vs. 0.868), which on the 5,000-patch K16 test set amounts to roughly 50 patches. Without repeated seeds or a paired comparison, this margin cannot be distinguished from training noise. Please report the mean and standard deviation over at least three seeds and, if possible, a paired test across identical test folds or a confidence interval for the accuracy difference.
[Methodology (loss formulation)] The manuscript never specifies the relative weight of the two loss terms; the text only says the overall loss combines the classification loss and the MSE consistency loss, but no value of a balancing hyperparameter lambda is given. Similarly, the perturbation range applied to the Vahadane stain concentrations and the exact protocol for producing the six augmented images are not defined. Without these details, the benchmark results are not reproducible, and the claim that the method is 'less computationally expensive' than IMPaSh cannot be evaluated because no runtime, FLOPs, or memory measurements are provided. Please state all hyperparameters and include a quantitative efficiency comparison.
[Methodology / Results] The method assumes that augmentations obtained by perturbing the K19 stain matrix cover the stain statistics of the target domain K16, but the paper provides no evidence for this. The text shows sample target patches but no analysis of the stain-vector distribution, no side-by-side augmented-versus-target examples, and no sensitivity analysis over the perturbation magnitude. If the perturbations are too narrow, the learned invariance may not match the actual domain shift; if too broad, the MSE loss may remove class-discriminative information. Please include a quantitative overlap measure between augmented and target stain statistics, or at least a qualitative comparison, together with a sensitivity study of the perturbation strength.

minor comments (5)

[References] Reference [13] (Stain-AgLr) is listed in the bibliography but never cited in the body; either cite it in the related work or remove it.
[Abstract / Introduction] The spelling is inconsistent ('regularisation' vs. 'regularization') and the introduction contains a typo ('muti-source'); the phrase 'begs the question' is also misused where 'raises the question' would be correct.
[Results and Discussions] The state-of-the-art claim is based on a single comparison with IMPaSh; the authors do not compare with recent stain-augmentation or stain-consistency methods such as StainMixUp [9] or Stain-AgLr [13]. Adding these comparisons would better contextualize the claim.
[Figure 1] The caption of Figure 1 does not explain the individual panels or indicate which images are original, augmented, or from the target domain; please label subfigures and describe each panel.
[Abstract] The abstract describes the consistency loss only as a 'stain regularisation loss'; stating explicitly that it is an MSE loss between source and augmented feature representations would make the abstract more informative.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the central result is an external benchmark accuracy, not a fitted or self-defined quantity.

full rationale

The paper's derivation chain is empirical rather than definitional. It trains a ResNet-18 feature extractor with cross-entropy classification loss and an MSE consistency loss between feature representations of source and stain-augmented images, where augmentations are produced by perturbing Vahadane stain matrices. The reported K19-to-K16 accuracy is measured on an external target dataset whose labels are not used during training, so the 87.8% result is not constructed from the model's own inputs. No fitted parameter is renamed as a prediction: the number of augmentations (6) and the combined loss weighting are training hyperparameters, and the paper does not tune them against K16 test accuracy. The comparison with IMPaSh is a benchmark against a previously published method, and although some cited works share authors with this paper (e.g., IMPaSh, TIAToolbox), they are used as baselines or software tools, not as load-bearing justifications for the claimed stain invariance. A missing ablation of the MSE consistency loss versus stain augmentation alone is a legitimate methodological concern, but an omitted ablation is not circularity because the claim remains falsifiable by external test accuracy. Therefore, no circular step is exhibited and the score is 0.

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

The central claim rests on several domain assumptions: that stain perturbation approximates real target variations, that ResNet-18 ImageNet features are a suitable starting point, and that MSE consistency does not destroy class structure. These are not validated beyond the single test-set accuracy. No new physical or methodological entities are introduced.

free parameters (3)

lambda (weight of MSE stain regularization loss) = not reported
The overall loss is written as L = L_c + lambda * L_r, but the value of lambda is not stated and no sensitivity analysis is provided.
number of stain-augmented images per input = 6
The paper states that 6 augmented images are generated for each input; this choice is not motivated or ablated.
stain perturbation magnitude range = not reported
The concentration perturbation parameters from TIAToolbox are not specified; the range of induced stain variation is a free choice.

assumptions (4)

domain assumption Vahadane stain separation and perturbation produce realistic stain variations
The method relies on Vahadane sparse stain separation (ref [18]) and TIAToolbox perturbation (ref [15]) to create augmented images; the faithfulness of these augmented images to real stain variation is assumed.
domain assumption ResNet-18 features initialized from ImageNet transfer to histology
The feature extractor is ResNet-18, presumably ImageNet-pretrained; the paper does not discuss the domain gap between natural images and histology.
ad hoc to paper MSE feature consistency preserves class-discriminative information
The stain regularization loss pushes source and augmented features together; the paper assumes this does not collapse features across classes, but no analysis of feature geometry is provided.
domain assumption K16 and K19 labels can be relabeled into a common 7-class scheme
The paper follows the relabeling strategy of [11]; if label correspondences are imperfect, the accuracy comparison is affected.

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Cite this review

Pith. "Pith review of Stain-Invariant Representation for Tissue Classification in Histology Images." pith.science (2026). https://pith.science/paper/NZA2XI4T

@misc{pith2026241115237,
  author       = {Pith},
  title        = {Pith review of: Stain-Invariant Representation for Tissue Classification in Histology Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZA2XI4T}},
  note         = {Machine review of arXiv:2411.15237}
}

read the original abstract

The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tissue type. This variability constitutes a domain shift and results in significant problems when training and testing deep learning (DL) algorithms in multi-cohort settings. As such, developing robust and generalisable DL models in computational pathology (CPath) remains an open challenge. In this regard, we propose a framework that generates stain-augmented versions of the training images using stain matrix perturbation. Thereafter, we employed a stain regularisation loss to enforce consistency between the feature representations of the source and augmented images. Doing so encourages the model to learn stain-invariant and, consequently, domain-invariant feature representations. We evaluate the performance of the proposed model on cross-domain multi-class tissue type classification of colorectal cancer images and have achieved improved performance compared to other state-of-the-art methods.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 24 canonical work pages

[24]

and Van Der Laak, J., 2019

Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.M., Ciompi, F. and Van Der Laak, J., 2019. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical image analysis, 58, p.101544

work page 2019
[1]

and Zhang, D., 2022

Wu, Y., Cheng, M., Huang, S., Pei, Z., Zuo, Y., Liu, J., Yang, K., Zhu, Q., Zhang, J., Hong, H. and Zhang, D., 2022. Recent advances of deep learning for computational histopathology: Principles and applications. Cancers, 14(5), p.1199

work page 2022
[2]

and Kather, J.N., 2021

Echle, A., Rindtorff, N.T., Brinker, T.J., Luedde, T., Pearson, A.T. and Kather, J.N., 2021. Deep learning in cancer pathology: a new generation of clinical biomarkers. British journal of cancer, 124(4), pp.686-696

work page 2021
[3]

and Lundström, C., 2020

Stacke, K., Eilertsen, G., Unger, J. and Lundström, C., 2020. Measuring domain shift for deep learning in histopathology. IEEE journal of biomedical and health informatics, 25(2), pp.325-336. 27th Conference on Medical Image Understanding and Analysis 2023 247 frontiersin.org

work page 2020
[4]

and Albarqouni, S., 2019, April

Shaban, M.T., Baur, C., Navab, N. and Albarqouni, S., 2019, April. Staingan: Stain style transfer for digital histological images. In 2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019) (pp. 953-956). IEEE

work page 2019
[5]

and Yang, Y., 2022, December

Jia, Q., Guo, J., Du, F., Yang, P. and Yang, Y., 2022, December. A Fast Texture-to-Stain Adversarial Stain Normalization Network for Histopathological Images. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2294-2301). IEEE

work page 2022
[6]

and Liang, L., 2022

Zhao, B., Han, C., Pan, X., Lin, J., Yi, Z., Liang, C., Chen, X., Li, B., Qiu, W., Li, D. and Liang, L., 2022. RestainNet: a self-supervised digital re-stainer for stain normalization. Computers and Electrical Engineering, 103, p.108304

work page 2022
[7]

and Rajpoot, N., 2022

Jahanifar, M., Shepard, A., Zamanitajeddin, N., Bashir, R.S., Bilal, M., Khurram, S.A., Minhas, F. and Rajpoot, N., 2022. Stain-robust mitotic figure detection for the mitosis domain generalization challenge. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2R...

work page 2022

Show all 25 references

[8]

and Rubin, D.L., 2021

Yamashita, R., Long, J., Banda, S., Shen, J. and Rubin, D.L., 2021. Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation. IEEE Transactions on Medical Imaging, 40(12), pp.3945-3954

2021
[9]

and Yeh, C.Y., 2021

Chang, J.R., Wu, M.S., Yu, W.H., Chen, C.C., Yang, C.K., Lin, Y.Y. and Yeh, C.Y., 2021. Stain mix-up: Unsupervised domain generalization for histopathology images. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbo...

2021
[10]

and Zhang, M., 2016

Ghifary, M., Balduzzi, D., Kleijn, W.B. and Zhang, M., 2016. Scatter component analysis: A unified framework for domain adaptation and domain generalization. IEEE transactions on pattern analysis and machine intelligence, 39(7), pp.1414- 1430

2016
[11]

and Thiran, J.P., 2022

Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B. and Thiran, J.P., 2022. Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection. Medical image analysis, 79, p.102473

2022
[12]

and Rajpoot, N., 2023, February

Vuong, T.T.L., Vu, Q.D., Jahanifar, M., Graham, S., Kwak, J.T. and Rajpoot, N., 2023, February. IMPaSh: A Novel Domain-Shift Resistant Representation for Colorectal Cancer Tissue Classification. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Pro...

2023
[13]

and Singhal, N., 2022, September

Raipuria, G., Shrivastava, A. and Singhal, N., 2022, September. Stain- AgLr: Stain Agnostic Learning for Computational Histopathology Using Domain Consistency and Stain Regeneration Loss. In Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in...

2022
[14]

and Hamarneh, G., 2023, February

Pakzad, A., Abhishek, K. and Hamarneh, G., 2023, February. CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IV (pp. 203-219). Cham: Spri...

2023
[15]

and Epstein, D., 2021

Pocock, J., Graham, S., Vu, Q.D., Jahanifar, M., Deshpande, S., Hadjigeorghiou, G., Shephard, A., Saad Bashir, R.M., Bilal, M., Lu, W. and Epstein, D., 2021. TIAToolbox: an end-to-end toolbox for advanced tissue image analytics. bioRxiv, pp.2021-12 27th Conference on Medical I...

2021
[16]

and Jansen, L.,

Kather, J.N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.A., Gaiser, T., Marx, A., Valous, N.A., Ferber, D. and Jansen, L.,
[17]

and Zöllner, F.G., 2016

Kather, J.N., Weis, C.A., Bianconi, F., Melchers, S.M., Schad, L.R., Gaiser, T., Marx, A. and Zöllner, F.G., 2016. Multi-class texture analysis in colorectal cancer histology. Scientific reports, 6(1), pp.1-11

2016
[18]

and Navab, N., 2016

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

2016
[19]

and Thomas, N.E., 2009, June

Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C. and Thomas, N.E., 2009, June. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE international symposium on biomedical imaging: from nano to macro (pp. 1...

2009
[20]

and Lin, D., 2018

Wu, Z., Xiong, Y., Yu, S.X. and Lin, D., 2018. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3733- 3742)

2018
[21]

and Maaten, L.V.D., 2020

Misra, I. and Maaten, L.V.D., 2020. Self-supervised learning of pretext- invariant representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6707-6717)

2020
[22]

and He, K., 2020

Chen, X., Fan, H., Girshick, R. and He, K., 2020. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297

2020 arXiv
[23]

and Isola, P., 2020

Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C. and Isola, P., 2020. What makes for good views for contrastive learning?. Advances in neural information processing systems, 33, pp.6827-6839. 250 27th Conference on Medical Image Understanding and Analysis 2023 frontiersin.org

2020
[2019]

PLoS medicine, 16(1), p.e1002730

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine, 16(1), p.e1002730

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [24]

and Van Der Laak, J., 2019

Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.M., Ciompi, F. and Van Der Laak, J., 2019. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical image analysis, 58, p.101544

work page 2019

[2] [1]

and Zhang, D., 2022

Wu, Y., Cheng, M., Huang, S., Pei, Z., Zuo, Y., Liu, J., Yang, K., Zhu, Q., Zhang, J., Hong, H. and Zhang, D., 2022. Recent advances of deep learning for computational histopathology: Principles and applications. Cancers, 14(5), p.1199

work page 2022

[3] [2]

and Kather, J.N., 2021

Echle, A., Rindtorff, N.T., Brinker, T.J., Luedde, T., Pearson, A.T. and Kather, J.N., 2021. Deep learning in cancer pathology: a new generation of clinical biomarkers. British journal of cancer, 124(4), pp.686-696

work page 2021

[4] [3]

and Lundström, C., 2020

Stacke, K., Eilertsen, G., Unger, J. and Lundström, C., 2020. Measuring domain shift for deep learning in histopathology. IEEE journal of biomedical and health informatics, 25(2), pp.325-336. 27th Conference on Medical Image Understanding and Analysis 2023 247 frontiersin.org

work page 2020

[5] [4]

and Albarqouni, S., 2019, April

Shaban, M.T., Baur, C., Navab, N. and Albarqouni, S., 2019, April. Staingan: Stain style transfer for digital histological images. In 2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019) (pp. 953-956). IEEE

work page 2019

[6] [5]

and Yang, Y., 2022, December

Jia, Q., Guo, J., Du, F., Yang, P. and Yang, Y., 2022, December. A Fast Texture-to-Stain Adversarial Stain Normalization Network for Histopathological Images. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2294-2301). IEEE

work page 2022

[7] [6]

and Liang, L., 2022

Zhao, B., Han, C., Pan, X., Lin, J., Yi, Z., Liang, C., Chen, X., Li, B., Qiu, W., Li, D. and Liang, L., 2022. RestainNet: a self-supervised digital re-stainer for stain normalization. Computers and Electrical Engineering, 103, p.108304

work page 2022

[8] [7]

and Rajpoot, N., 2022

Jahanifar, M., Shepard, A., Zamanitajeddin, N., Bashir, R.S., Bilal, M., Khurram, S.A., Minhas, F. and Rajpoot, N., 2022. Stain-robust mitotic figure detection for the mitosis domain generalization challenge. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2R...

work page 2022