@article{moens2026hallucinates,
  title        = {When normalization hallucinates: unseen risks in AI-powered whole slide image processing},
  author       = {Moens, Karel and Blaschko, Matthew B and Tuytelaars, Tinne and Diricx, Bart and De Vylder, Jonas and Yousif, Mustafa},
  journal      = {arXiv:2512.07426 preprint Proc. SPIE Medical Imaging},
  url          = {https://doi.org/10.48550/arXiv.2512.07426},
  year         = {2026}
}

@inproceedings{shaban2019staingan,
  title        = {Staingan: Stain style transfer for digital histological images},
  author       = {Shaban, M Tarek and Baur, Christoph and Navab, Nassir and Albarqouni, Shadi},
  booktitle    = {2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019)},
  pages        = {953--956},
  year         = {2019},
  organization = {IEEE}
}

@article{stainnet,
  author   = {Kang, Hongtao  and Luo, Die  and Feng, Weihua  and Zeng, Shaoqun  and Quan, Tingwei  and Hu, Junbo  and Liu, Xiuli },
  title    = {StainNet: A Fast and Robust Stain Normalization Network},
  journal  = {Frontiers in Medicine},
  volume   = {8},
  year     = {2021},
  url      = {https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.746307},
  doi      = {10.3389/fmed.2021.746307},
  issn     = {2296-858X},
  abstract = {<p>Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.</p>}
}

@article{wassersteingp,
  title   = {Improved training of wasserstein gans},
  author  = {Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron C},
  journal = {Advances in neural information processing systems},
  volume  = {30},
  year    = {2017}
}

@article{instablegan,
  author     = {Jabbar, Abdul and Li, Xi and Omar, Bourahla},
  title      = {A Survey on Generative Adversarial Networks: Variants, Applications, and Training},
  year       = {2021},
  issue_date = {November 2022},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {54},
  number     = {8},
  issn       = {0360-0300},
  url        = {https://doi.org/10.1145/3463475},
  doi        = {10.1145/3463475},
  abstract   = {The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.},
  journal    = {ACM Comput. Surv.},
  month      = oct,
  articleno  = {157},
  numpages   = {49},
  keywords   = {stabilize training, applications, architectural-variants, Generative Adversarial Networks (GANs)}
}

@article{Wiatrak2019StabilizingGA,
  title   = {Stabilizing Generative Adversarial Networks: A Survey},
  author  = {Maciej Wiatrak and Stefano V. Albrecht and Andrew Nystrom},
  journal = {arXiv: Learning},
  year    = {2019},
  url     = {https://api.semanticscholar.org/CorpusID:216355573}
}

@inproceedings{Roux2014,
  author    = {L. Roux},
  title     = {Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images},
  booktitle = {Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014)},
  year      = {2014}
}

@article{midog,
  title    = {Mitosis domain generalization in histopathology images — The MIDOG challenge},
  journal  = {Medical Image Analysis},
  volume   = {84},
  pages    = {102699},
  year     = {2023},
  issn     = {1361-8415},
  doi      = {https://doi.org/10.1016/j.media.2022.102699},
  url      = {https://www.sciencedirect.com/science/article/pii/S1361841522003279},
  author   = {Marc Aubreville and Nikolas Stathonikos and Christof A. Bertram and Robert Klopfleisch and Natalie {ter Hoeve} and Francesco Ciompi and Frauke Wilm and Christian Marzahl and Taryn A. Donovan and Andreas Maier and Jack Breen and Nishant Ravikumar and Youjin Chung and Jinah Park and Ramin Nateghi and Fattaneh Pourakpour and Rutger H.J. Fick and Saima {Ben Hadj} and Mostafa Jahanifar and Adam Shephard and Jakob Dexl and Thomas Wittenberg and Satoshi Kondo and Maxime W. Lafarge and Viktor H. Koelzer and Jingtang Liang and Yubo Wang and Xi Long and Jingxin Liu and Salar Razavi and April Khademi and Sen Yang and Xiyue Wang and Ramona Erber and Andrea Klang and Karoline Lipnik and Pompei Bolfa and Michael J. Dark and Gabriel Wasinger and Mitko Veta and Katharina Breininger},
  keywords = {Domain generalization, Histopathology, Challenge, Deep Learning, Mitosis},
  abstract = {The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.}
}

@inproceedings{Shrivastava2021,
  author    = {A. Shrivastava and W. Adorno and Y. Sharma and L. Ehsan and S. A. Ali and S. R. Moore and others},
  title     = {Self-attentive adversarial stain normalization},
  booktitle = {Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021},
  year      = {2021},
  pages     = {12040},
  doi       = {10.1007/978-3-030-68763-2_10}
}

@article{stacke2020measuring,
  title     = {Measuring domain shift for deep learning in histopathology},
  author    = {Stacke, Karin and Eilertsen, Gabriel and Unger, Jonas and Lundstr{\"o}m, Claes},
  journal   = {IEEE journal of biomedical and health informatics},
  volume    = {25},
  number    = {2},
  pages     = {325--336},
  year      = {2020},
  publisher = {IEEE}
}

@article{bai2023deep,
  title     = {Deep learning-enabled virtual histological staining of biological samples},
  author    = {Bai, Bijie and Yang, Xilin and Li, Yuzhu and Zhang, Yijie and Pillar, Nir and Ozcan, Aydogan},
  journal   = {Light: Science \& Applications},
  volume    = {12},
  number    = {1},
  pages     = {57},
  year      = {2023},
  publisher = {Nature Publishing Group UK London}
}

@article{alber2025novel,
  title   = {A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics},
  author  = {Alber, Maximilian and Tietz, Stephan and Dippel, Jonas and Milbich, Timo and Lesort, Timoth{\'e}e and Korfiatis, Panos and Kr{\"u}gener, Moritz and Cancer, Beatriz Perez and Shah, Neelay and M{\"o}llers, Alexander and others},
  journal = {arXiv preprint arXiv:2501.05409},
  year    = {2025}
}

@article{wang2022transformer,
  title     = {Transformer-based unsupervised contrastive learning for histopathological image classification},
  author    = {Wang, Xiyue and Yang, Sen and Zhang, Jun and Wang, Minghui and Zhang, Jing and Yang, Wei and Huang, Junzhou and Han, Xiao},
  journal   = {Medical image analysis},
  volume    = {81},
  pages     = {102559},
  year      = {2022},
  publisher = {Elsevier}
}

@inproceedings{stegmuller2023scorenet,
  title     = {Scorenet: Learning non-uniform attention and augmentation for transformer-based histopathological image classification},
  author    = {Stegm{\"u}ller, Thomas and Bozorgtabar, Behzad and Spahr, Antoine and Thiran, Jean-Philippe},
  booktitle = {Proceedings of the IEEE/CVF winter Conference on applications of computer vision},
  pages     = {6170--6179},
  year      = {2023}
}

@article{tellez2019quantifying,
  title     = {Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology},
  author    = {Tellez, David and Litjens, Geert and B{\'a}ndi, P{\'e}ter and Bulten, Wouter and Bokhorst, John-Melle and Ciompi, Francesco and Van Der Laak, Jeroen},
  journal   = {Medical image analysis},
  volume    = {58},
  pages     = {101544},
  year      = {2019},
  publisher = {Elsevier}
}

@inproceedings{moghadam2023morphology,
  title     = {A morphology focused diffusion probabilistic model for synthesis of histopathology images},
  author    = {Moghadam, Puria Azadi and Van Dalen, Sanne and Martin, Karina C and Lennerz, Jochen and Yip, Stephen and Farahani, Hossein and Bashashati, Ali},
  booktitle = {Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages     = {2000--2009},
  year      = {2023}
}

@article{ustinova2016learning,
  title   = {Learning deep embeddings with histogram loss},
  author  = {Ustinova, Evgeniya and Lempitsky, Victor},
  journal = {Advances in neural information processing systems},
  volume  = {29},
  year    = {2016}
}

@inproceedings{afifi2021histogan,
  title     = {Histogan: Controlling colors of gan-generated and real images via color histograms},
  author    = {Afifi, Mahmoud and Brubaker, Marcus A and Brown, Michael S},
  booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages     = {7941--7950},
  year      = {2021}
}

@inproceedings{seguy2018large,
  title     = {Large-Scale Optimal Transport and Mapping Estimation},
  author    = {Seguy, Vivien and Damodaran, Bharath Bhushan and Flamary, Remi and Courty, Nicolas and Rolet, Antoine and Blondel, Mathieu},
  booktitle = {ICLR 2018-International Conference on Learning Representations},
  pages     = {1--15},
  year      = {2018}
}

@article{lee2022stain,
  title     = {Stain style transfer for histological images using S3CGAN},
  author    = {Lee, Jiann-Shu and Ma, Yao-Xian},
  journal   = {Sensors},
  volume    = {22},
  number    = {3},
  pages     = {1044},
  year      = {2022},
  publisher = {MDPI}
}

@inproceedings{Legala2025,
  author       = {Ashwitha Legala and Sonal Shukla and Parul Khare and Brandon Veremis and Kenneth Aschheim and Margaret Brandwein and Falk Schwendicke and Pemith Liyanage and Marelina Vered and Supriya Kheur and Aravindha Babu and Radhika M Bavle and Beatriz Aldape and Rodrigo Sanchez and Filipe Fonseca and Cinthia Caceres and Rima Safadi and Abdelmajid Mansour and Nabil Kochaji and Mohamed Mostafa and Florence Cuadra and Nii Nartey and Abena Nsiah and Scott Doyle},
  title        = {Quantification and mitigation of site-specific differences in digital pathology datasets},
  booktitle    = {Proceedings of SPIE 2025},
  year         = {2025},
  month        = {February},
  day          = {20},
  address      = {Sunset 1/2},
  organization = {SPIE},
  note         = {Paper 13413-30, presented at SPIE 2025 Conference, 20 February 2025, 10:50 AM - 11:10 AM PST}
}

@inproceedings{Gangeh2025,
  author       = {Mehrdad Jabbarzadeh Gangeh and Arash Moayyedi and Nazim Shaikh and Xingwei Wang and Yao Nie},
  title        = {Out-of-distribution generalization based on normalization techniques in histopathology whole slide imaging},
  booktitle    = {Proceedings of SPIE 2025},
  year         = {2025},
  month        = {February},
  day          = {20},
  address      = {Sunset 1/2},
  organization = {SPIE},
  note         = {Paper 13413-29, presented at SPIE 2025 Conference, 20 February 2025, 10:30 AM - 10:50 AM PST}
}

@inproceedings{nguyencontrimix,
  title     = {ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology},
  author    = {Nguyen, Tan H and Juyal, Dinkar and Li, Jin and Prakash, Aaditya and Nofallah, Shima and Shah, Chintan and Gullapally, Sai Chowdary and Yu, Limin and Griffin, Michael and Sampat, Anand and others},
  booktitle = {MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL)},
  year      = {2024}
}

@article{bejnordi2017diagnostic,
  title     = {Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer},
  author    = {Bejnordi, Babak Ehteshami and Veta, Mitko and Van Diest, Paul Johannes and Van Ginneken, Bram and Karssemeijer, Nico and Litjens, Geert and Van Der Laak, Jeroen AWM and Hermsen, Meyke and Manson, Quirine F and Balkenhol, Maschenka and others},
  journal   = {JAMA-Journal of the American Medical Association},
  volume    = {318},
  number    = {22},
  pages     = {2199--2210},
  year      = {2017},
  publisher = {American Medical Association}
}

@article{iandola2016squeezenet,
  title   = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size},
  author  = {Iandola, Forrest N and Han, Song and Moskewicz, Matthew W and Ashraf, Khalid and Dally, William J and Keutzer, Kurt},
  journal = {arXiv preprint arXiv:1602.07360},
  year    = {2016}
}

@inproceedings{shen2023staindiff,
  title        = {Staindiff: Transfer stain styles of histology images with denoising diffusion probabilistic models and self-ensemble},
  author       = {Shen, Yiqing and Ke, Jing},
  booktitle    = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages        = {549--559},
  year         = {2023},
  organization = {Springer}
}

@article{jewsbury2024stainfuser,
  title   = {StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images},
  author  = {Jewsbury, Robert and Wang, Ruoyu and Bhalerao, Abhir and Rajpoot, Nasir and Vu, Quoc Dang},
  journal = {arXiv preprint arXiv:2403.09302},
  year    = {2024}
}

@inproceedings{wagner2021structure,
  title        = {Structure-preserving multi-domain stain color augmentation using style-transfer with disentangled representations},
  author       = {Wagner, Sophia J and Khalili, Nadieh and Sharma, Raghav and Boxberg, Melanie and Marr, Carsten and De Back, Walter and Peng, Tingying},
  booktitle    = {Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part VIII 24},
  pages        = {257--266},
  year         = {2021},
  organization = {Springer}
}

@inproceedings{shen2022randstainna,
  title        = {Randstainna: Learning stain-agnostic features from histology slides by bridging stain augmentation and normalization},
  author       = {Shen, Yiqing and Luo, Yulin and Shen, Dinggang and Ke, Jing},
  booktitle    = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages        = {212--221},
  year         = {2022},
  organization = {Springer}
}

@article{swiderska2020impact,
  title     = {Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer},
  author    = {Swiderska-Chadaj, Zaneta and de Bel, Thomas and Blanchet, Lionel and Baidoshvili, Alexi and Vossen, Dirk and van der Laak, Jeroen and Litjens, Geert},
  journal   = {Scientific Reports},
  volume    = {10},
  number    = {1},
  pages     = {14398},
  year      = {2020},
  publisher = {Nature Publishing Group UK London}
}

@article{vasiljevic2022cyclegan,
  title     = {CycleGAN for virtual stain transfer: Is seeing really believing?},
  author    = {Vasiljevi{\'c}, Jelica and Nisar, Zeeshan and Feuerhake, Friedrich and Wemmert, C{\'e}dric and Lampert, Thomas},
  journal   = {Artificial Intelligence in Medicine},
  volume    = {133},
  pages     = {102420},
  year      = {2022},
  publisher = {Elsevier}
}

@article{reinhard2001color,
  title     = {Color transfer between images},
  author    = {Reinhard, Erik and Adhikhmin, Michael and Gooch, Bruce and Shirley, Peter},
  journal   = {IEEE Computer graphics and applications},
  volume    = {21},
  number    = {5},
  pages     = {34--41},
  year      = {2001},
  publisher = {IEEE}
}

@inproceedings{gao2022out,
  title     = {Out-of-distribution robustness via targeted augmentations},
  author    = {Gao, Irena and Sagawa, Shiori and Koh, Pang Wei and Hashimoto, Tatsunori and Liang, Percy},
  booktitle = {NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications},
  year      = {2022}
}

@article{jahanifar2025domain,
  title     = {Domain generalization in computational pathology: survey and guidelines},
  author    = {Jahanifar, Mostafa and Raza, Manahil and Xu, Kesi and Vuong, Trinh Thi Le and Jewsbury, Robert and Shephard, Adam and Zamanitajeddin, Neda and Kwak, Jin Tae and Raza, Shan E Ahmed and Minhas, Fayyaz and others},
  journal   = {ACM Computing Surveys},
  volume    = {57},
  number    = {11},
  pages     = {1--37},
  year      = {2025},
  publisher = {ACM New York, NY}
}

@inproceedings{dawood2023tissue,
  title     = {Do tissue source sites leave identifiable signatures in whole slide images beyond staining?},
  author    = {Dawood, Muhammad and Keller, Piotr and others},
  booktitle = {ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare},
  year      = {2023}
}

@article{howard2021impact,
  title     = {The impact of site-specific digital histology signatures on deep learning model accuracy and bias},
  author    = {Howard, Frederick M and Dolezal, James and Kochanny, Sara and Schulte, Jefree and Chen, Heather and Heij, Lara and Huo, Dezheng and Nanda, Rita and Olopade, Olufunmilayo I and Kather, Jakob N and others},
  journal   = {Nature communications},
  volume    = {12},
  number    = {1},
  pages     = {4423},
  year      = {2021},
  publisher = {Nature Publishing Group UK London}
}

@article{Clark2013TCIA,
  author    = {Clark, K. and Vendt, B. and Smith, K. and Freymann, J. and Kirby, J. and Koppel, P. and Moore, S. and Phillips, S. and Maffitt, D. and Pringle, M. and Tarbox, L. and Prior, F.},
  title     = {The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  journal   = {Journal of Digital Imaging},
  volume    = {26},
  number    = {6},
  pages     = {1045--1057},
  year      = {2013},
  month     = dec,
  doi       = {10.1007/s10278-013-9622-7},
  pmid      = {23884657},
  pmcid     = {PMC3824915}
}

@article{vahadane2016structure,
  title={Structure-preserving color normalization and sparse stain separation for histological images},
  author={Vahadane, Abhishek and Peng, Tingying and Sethi, Amit and Albarqouni, Shadi and Wang, Lichao and Baust, Maximilian and Steiger, Katja and Schlitter, Anna Melissa and Esposito, Irene and Navab, Nassir},
  journal={IEEE transactions on medical imaging},
  volume={35},
  number={8},
  pages={1962--1971},
  year={2016},
  publisher={IEEE}
}

@article{yang2025sastaindiff,
  title={SAStainDiff: Self-supervised stain normalization by stain augmentation using denoising diffusion probabilistic models},
  author={Yang, Huaishui and Lyu, Mengye and Yan, Shiyue and Zhong, Tianzhao and Li, Jihao and Xu, Tong and Xie, Huhan and Liu, Shaojun},
  journal={Biomedical Signal Processing and Control},
  volume={107},
  pages={107861},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{doerrich2024self,
  title={Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization},
  author={Doerrich, Sebastian and Di Salvo, Francesco and Ledig, Christian},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={644--654},
  year={2024},
  organization={Springer}
}
