% Artifacts in DP 
@article{TELLEZ2019101544,
title = {Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology},
journal = {Medical Image Analysis},
volume = {58},

year = {2019},
issn = {1361-8415},
author = {David Tellez and Geert Litjens and Péter Bándi and Wouter Bulten and John-Melle Bokhorst and Francesco Ciompi and Jeroen {van der Laak}},
keywords = {Deep learning, Convolutional neural network, Computational pathology},
}
%Yuri artifacts
@article{schomig2021quality,
  title={Quality control stress test for deep learning-based diagnostic model in digital pathology},
  author={Sch{\"o}mig-Markiefka, Birgid and Pryalukhin, Alexey and Hulla, Wolfgang and Bychkov, Andrey and Fukuoka, Junya and Madabhushi, Anant and Achter, Viktor and Nieroda, Lech and B{\"u}ttner, Reinhard and Quaas, Alexander and others},
  journal={Modern Pathology},
  year={2021},
  publisher={Nature Publishing Group}
}
% Froodo
@article{stieber2022froodo,
  title={FrOoDo: Framework for Out-of-Distribution Detection},
  author={Stieber, Jonathan and Fuchs, Moritz and Mukhopadhyay, Anirban},
  journal={arXiv preprint arXiv:2208.00963},
  year={2022},
  publisher={arXiv}
}
% another stress test
@article{wang2021stress,
  title={Stress testing pathology models with generated artifacts},
  author={Wang, Nicholas Chandler and Kaplan, Jeremy and Lee, Joonsang and Hodgin, Jeffrey and Udager, Aaron and Rao, Arvind},
  journal={Journal of Pathology Informatics},
  volume={12},
  number={1},
  pages={54},
  year={2021},
  publisher={Elsevier}
}
% Niklas
@article{babendererde2023jointly,
  title={Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning},
  author={Babendererde, Niklas and Fuchs, Moritz and Gonzalez, Camila and Tolkach, Yuri and Mukhopadhyay, Anirban},
  journal={arXiv preprint arXiv:2309.00688},
  year={2023}
}
% WSI acquistion processe
@article{kanwal2022devil,
  title={The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review},
  author={Kanwal, Neel and P{\'e}rez-Bueno, Fernando and Schmidt, Arne and Engan, Kjersti and Molina, Rafael},
  journal={IEEE Access},
  year={2022},
  publisher={IEEE}
}
%ddpm/RePaint with artefact supervision
@inproceedings{he2023artifact,
  title={Artifact Restoration in Histology Images with Diffusion Probabilistic Models},
  author={He, Zhenqi and He, Junjun and Ye, Jin and Shen, Yiqing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={518--527},
  year={2023},
  organization={Springer}
}
% ARcycleGAN with weak supervision
@article{ke2023artifact,
  title={Artifact Detection and Restoration in Histology Images with Stain-Style and Structural Preservation},
  author={Ke, Jing and Liu, Kai and Sun, Yuxiang and Xue, Yuying and Huang, Jiaxuan and Lu, Yizhou and Dai, Jun and Chen, Yaobing and Han, Xiaodan and Shen, Yiqing and others},
  journal={IEEE Transactions on Medical Imaging},
  year={2023},
  publisher={IEEE}
}
@misc{eu_AIAct_2016,
	title = {proposal for a Regulation of the European Parlia-
ment and of the Council laying down harmonised rules on artificial intel-
ligence (Artificial Intelligence Act) and amending certain Union legisla-
tive acts (COM(2021) 206 final)},
	shorttitle = {{EU AI Act}},
	url = {https://data.consilium.europa.eu/doc/document/ST-5662-2024-INIT/en/pdf},
	author = {EU},
	month = Jan,
	year = {2021},
	pages = {1--272},
}
% CycleGAN with supervison
@article{dahan2022artifact,
  title={Artifact Removal in Histopathology Images},
  author={Dahan, Cameron and Christodoulidis, Stergios and Vakalopoulou, Maria and Boyd, Joseph},
  journal={arXiv preprint arXiv:2211.16161},
  year={2022}
}
% drifts correction w/o visual output/representation
@article{roschewitz2023automatic,
  title={Automatic correction of performance drift under acquisition shift in medical image classification},
  author={Roschewitz, M{\'e}lanie and Khara, Galvin and Yearsley, Joe and Sharma, Nisha and James, Jonathan J and Ambr{\'o}zay, {\'E}va and Heroux, Adam and Kecskemethy, Peter and Rijken, Tobias and Glocker, Ben},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={6608},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
%BCSS
@article{amgad2019structured,
  title={Structured crowdsourcing enables convolutional segmentation of histology images},
  author={Amgad, Mohamed and Elfandy, Habiba and Hussein, Hagar and Atteya, Lamees A and Elsebaie, Mai AT and Abo Elnasr, Lamia S and Sakr, Rokia A and Salem, Hazem SE and Ismail, Ahmed F and Saad, Anas M and others},
  journal={Bioinformatics},
  year={2019},
  publisher={Oxford University Press}
}
%%%%%%%%%%%% Classical computer vision papers %%%%%%%%%%%%%%%%
% DDPM 
@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}
% RePaint
@inproceedings{lugmayr2022repaint,
  title={Repaint: Inpainting using denoising diffusion probabilistic models},
  author={Lugmayr, Andreas and Danelljan, Martin and Romero, Andres and Yu, Fisher and Timofte, Radu and Van Gool, Luc},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11461--11471},
  year={2022}
}
% palette for I2I conditional impainting
@inproceedings{saharia2022palette,
  title={Palette: Image-to-image diffusion models},
  author={Saharia, Chitwan and Chan, William and Chang, Huiwen and Lee, Chris and Ho, Jonathan and Salimans, Tim and Fleet, David and Norouzi, Mohammad},
  booktitle={ACM SIGGRAPH 2022 Conference Proceedings},
  pages={1--10},
  year={2022}
}
% Shadow removal
@inproceedings{zhang2020ris,
  title={Ris-gan: Explore residual and illumination with generative adversarial networks for shadow removal},
  author={Zhang, Ling and Long, Chengjiang and Zhang, Xiaolong and Xiao, Chunxia},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={07},
  pages={12829--12836},
  year={2020}
}
% DDRM
@article{kawar2022denoising,
  title={Denoising diffusion restoration models},
  author={Kawar, Bahjat and Elad, Michael and Ermon, Stefano and Song, Jiaming},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={23593--23606},
  year={2022}
}
% Inpainting review
@article{xiang2023deep,
  title={Deep learning for image inpainting: A survey},
  author={Xiang, Hanyu and Zou, Qin and Nawaz, Muhammad Ali and Huang, Xianfeng and Zhang, Fan and Yu, Hongkai},
  journal={Pattern Recognition},
  volume={134},
  pages={109046},
  year={2023},
  publisher={Elsevier}
}
%sAM
@article{kirillov2023segment,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  journal={arXiv preprint arXiv:2304.02643},
  year={2023}
}
%DBSCAN
@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei and others},
  booktitle={kdd},
  volume={96},
  number={34},
  pages={226--231},
  year={1996}
}
% Anomalib
@inproceedings{akcay2022anomalib,
  title={Anomalib: A deep learning library for anomaly detection},
  author={Akcay, Samet and Ameln, Dick and Vaidya, Ashwin and Lakshmanan, Barath and Ahuja, Nilesh and Genc, Utku},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={1706--1710},
  year={2022},
  organization={IEEE}
}
% STFPM
@article{wang2021student,
  title={Student-teacher feature pyramid matching for anomaly detection},
  author={Wang, Guodong and Han, Shumin and Ding, Errui and Huang, Di},
  journal={BMVC},
  year={2021}
}
% FastFlow
@article{yu2021fastflow,
  title={Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows},
  author={Yu, Jiawei and Zheng, Ye and Wang, Xiang and Li, Wei and Wu, Yushuang and Zhao, Rui and Wu, Liwei},
  journal={arXiv preprint arXiv:2111.07677},
  year={2021}
}
%DRAEM
@inproceedings{zavrtanik2021draem,
  title={Draem-a discriminatively trained reconstruction embedding for surface anomaly detection},
  author={Zavrtanik, Vitjan and Kristan, Matej and Sko{\v{c}}aj, Danijel},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8330--8339},
  year={2021}
}
% iNPAINTIN REVIEW
@article{zhang2023image,
  title={Image inpainting based on deep learning: A review},
  author={Zhang, Xiaobo and Zhai, Donghai and Li, Tianrui and Zhou, Yuxin and Lin, Yang},
  journal={Information Fusion},
  volume={90},
  pages={74--94},
  year={2023},
  publisher={Elsevier}
}
%Natural image inpaint gan
@InProceedings{Zhao_2020_CVPR,
author = {Zhao, Lei and Mo, Qihang and Lin, Sihuan and Wang, Zhizhong and Zuo, Zhiwen and Chen, Haibo and Xing, Wei and Lu, Dongming},
title = {UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
} 

% Stain normalization
@inproceedings{wagner2022federated,
  title={Federated Stain Normalization for Computational Pathology},
  author={Wagner, Nicolas and Fuchs, Moritz and Tolkach, Yuri and Mukhopadhyay, Anirban},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={14--23},
  year={2022},
  organization={Springer}
}
% Stain Normalization
@inproceedings{
faryna2021tailoring,
title={Tailoring automated data augmentation to H\&E-stained histopathology},
author={Khrystyna Faryna and Jeroen van der Laak and Geert Litjens},
booktitle={Medical Imaging with Deep Learning},
year={2021},
}