{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:34:50Z","timestamp":1780356890120,"version":"3.54.1"},"reference-count":263,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n            Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) for various tasks on multi-gigapixel histology images. Nevertheless, the presence of out-of-distribution data (stemming from different sources such as disparate imaging devices) can cause\n            <jats:italic>domain shift<\/jats:italic>\n            (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative\n            <jats:italic>domain generalization<\/jats:italic>\n            (DG) solutions. Recognizing the potential of DG to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG example problem. Our findings suggest that careful experiment design and Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish guidelines for detecting and managing DS in different scenarios. While most of the concepts and recommendations are given for applications in CPath, they apply to most medical image analysis tasks as well.\n          <\/jats:p>","DOI":"10.1145\/3724391","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T11:01:56Z","timestamp":1744801316000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Domain Generalization in Computational Pathology: Survey and Guidelines"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5842-0460","authenticated-orcid":false,"given":"Mostafa","family":"Jahanifar","sequence":"first","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5136-8513","authenticated-orcid":false,"given":"Manahil","family":"Raza","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4335-8447","authenticated-orcid":false,"given":"Kesi","family":"Xu","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0775-2884","authenticated-orcid":false,"given":"Trinh Thi Le","family":"Vuong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, Korea (the Republic of)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0495-8438","authenticated-orcid":false,"given":"Robert","family":"Jewsbury","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0969-2990","authenticated-orcid":false,"given":"Adam","family":"Shephard","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5309-1223","authenticated-orcid":false,"given":"Neda","family":"Zamanitajeddin","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0287-4097","authenticated-orcid":false,"given":"Jin Tae","family":"Kwak","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, Korea (the Republic of)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-1738","authenticated-orcid":false,"given":"Shan E Ahmed","family":"Raza","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-1189","authenticated-orcid":false,"given":"Fayyaz","family":"Minhas","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4706-1308","authenticated-orcid":false,"given":"Nasir","family":"Rajpoot","sequence":"additional","affiliation":[{"name":"Computer Science, University of Warwick, Coventry, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"3438","article-title":"Invariance principle meets information bottleneck for out-of-distribution generalization","volume":"34","author":"Ahuja Kartik","year":"2021","unstructured":"Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, and others. 2021. Invariance principle meets information bottleneck for out-of-distribution generalization. Advances in Neural Information Processing Systems 34 (2021), 3438\u20133450.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101771"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz083"},{"key":"e_1_3_2_5_2","unstructured":"Deepak Anand Gaurav Patel Yaman Dang and Amit Sethi. 2020. Switching loss for generalized nucleus detection in histopathology. arXiv:2008.03750. Retrieved from https:\/\/arxiv.org\/abs\/2008.03750"},{"key":"e_1_3_2_6_2","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. arXiv:1907.02893. Retrieved from https:\/\/arxiv.org\/abs\/1907.02893"},{"key":"e_1_3_2_7_2","article-title":"Unleashing the potential of AI for pathology: Challenges and recommendations","author":"Asif Amina","year":"2023","unstructured":"Amina Asif, Kashif Rajpoot, Simon Graham, David Snead, Fayyaz Minhas, and Nasir Rajpoot. 2023. Unleashing the potential of AI for pathology: Challenges and recommendations. The Journal of Pathology 260, 5 (2023), 564\u2013577.","journal-title":"The Journal of Pathology"},{"key":"e_1_3_2_8_2","volume-title":"Proceedings of the Medical Imaging with Deep Learning","author":"Aubreville Marc","year":"2021","unstructured":"Marc Aubreville. 2021. Quantifying the scanner-induced domain gap in mitosis detection. In Proceedings of the Medical Imaging with Deep Learning."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Marc Aubreville Christof Bertram Katharina Breininger Samir Jabari Nikolas Stathonikos and Mitko Veta. 2022. MItosis DOmain generalization challenge 2022 Zenodo (March 2022). DOI:10.5281\/zenodo.6362337","DOI":"10.5281\/zenodo.6362337"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102699"},{"issue":"1","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/s41597-023-02327-4","article-title":"A comprehensive multi-domain dataset for mitotic figure detection","volume":"10","author":"Aubreville Marc","year":"2023","unstructured":"Marc Aubreville, Frauke Wilm, Nikolas Stathonikos, and others. 2023. A comprehensive multi-domain dataset for mitotic figure detection. Scientific Data 10, 1 (2023), 484.","journal-title":"Scientific Data"},{"issue":"2","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Baltru\u0161aitis Tadas","year":"2018","unstructured":"Tadas Baltru\u0161aitis, Chaitanya Ahuja, and Louis-Philippe Morency. 2018. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 2 (2018), 423\u2013443.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2018.2867350"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","article-title":"A theory of learning from different domains","volume":"79","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine Learning 79 (2010), 151\u2013175.","journal-title":"Machine Learning"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/1273496.1273507","volume-title":"Proceedings of the 24th International Conference on Machine Learning","author":"Bickel Steffen","year":"2007","unstructured":"Steffen Bickel, Michael Br\u00fcckner, and Tobias Scheffer. 2007. Discriminative learning for differing training and test distributions. In Proceedings of the 24th International Conference on Machine Learning. 81\u201388."},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"102885","DOI":"10.1016\/j.media.2023.102885","article-title":"An aggregation of aggregation methods in computational pathology","author":"Bilal Mohsin","year":"2023","unstructured":"Mohsin Bilal, Robert Jewsbury, Ruoyu Wang, and others. 2023. An aggregation of aggregation methods in computational pathology. Medical Image Analysis (2023), 102885.","journal-title":"Medical Image Analysis"},{"issue":"12","key":"e_1_3_2_17_2","first-page":"e763\u2013e772","article-title":"Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: A retrospective study","volume":"3","author":"Bilal Mohsin","year":"2021","unstructured":"Mohsin Bilal, Shan E. Ahmed Raza, Ayesha Azam, and others. 2021. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: A retrospective study. The Lancet Digital Health 3, 12 (2021), e763\u2013e772.","journal-title":"The Lancet Digital Health"},{"issue":"1","key":"e_1_3_2_18_2","first-page":"46","article-title":"Domain generalization by marginal transfer learning","volume":"22","author":"Blanchard Gilles","year":"2021","unstructured":"Gilles Blanchard, Aniket Anand Deshmukh, \u00dcrun Dogan, Gyemin Lee, and Clayton Scott. 2021. Domain generalization by marginal transfer learning. The Journal of Machine Learning Research 22, 1 (2021), 46\u2013100.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_19_2","article-title":"Generalizing from several related classification tasks to a new unlabeled sample","volume":"24","author":"Blanchard Gilles","year":"2011","unstructured":"Gilles Blanchard, Gyemin Lee, and Clayton Scott. 2011. Generalizing from several related classification tasks to a new unlabeled sample. Advances in Neural Information Processing Systems 24 (2011), 1\u20139.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_20_2","unstructured":"Andrew A. Borkowski Marilyn M. Bui L. Brannon Thomas Catherine P. Wilson Lauren A. DeLand and Stephen M. Mastorides. 2019. Lung and colon cancer histopathological image dataset (lc25000). arXiv:1912.12142. Retrieved from https:\/\/arxiv.org\/abs\/1912.12142"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"105273","DOI":"10.1016\/j.cmpb.2019.105273","article-title":"Glomerulosclerosis identification in whole slide images using semantic segmentation","volume":"184","author":"Bueno Gloria","year":"2020","unstructured":"Gloria Bueno, M. Milagro Fernandez-Carrobles, Lucia Gonzalez-Lopez, and Oscar Deniz. 2020. Glomerulosclerosis identification in whole slide images using semantic segmentation. Computer Methods and Programs in Biomedicine 184 (2020), 105273.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","article-title":"Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge","volume":"28","author":"Bulten Wouter","year":"2022","unstructured":"Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen, and others. 2022. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge. Nature Medicine 28, 1 (2022), 154\u2013163.","journal-title":"Nature Medicine"},{"key":"e_1_3_2_23_2","unstructured":"Peter Byfield. 2019. StainTools: Tools for Tissue Image Stain Normalisation and Augmentation in Python 3. Retrieved July 31 2023 from https:\/\/github.com\/Peter554\/StainTools"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102755"},{"key":"e_1_3_2_25_2","first-page":"277","volume-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021","author":"Cai Jiatong","year":"2021","unstructured":"Jiatong Cai, Chenglu Zhu, Can Cui, and others. 2021. Generalizing nucleus recognition model in multi-source Ki67 immunohistochemistry stained images via domain-specific pruning. In Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021. Springer International Publishing, Cham, 277\u2013287. DOI:10.1007\/978-3-030-87237-3_27"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0612-7"},{"key":"e_1_3_2_27_2","unstructured":"Gabriele Campanella Shengjia Chen Ruchika Verma and others. 2024. A clinical benchmark of public self-supervised pathology foundation models. arXiv:2407.06508. Retrieved from https:\/\/arxiv.org\/abs\/2407.06508"},{"key":"e_1_3_2_28_2","first-page":"301","volume-title":"Proceedings of the 16th European Conference on Computer Vision\u2013ECCV 2020 Glasgow, UK, August 23\u201328, 2020","author":"Chattopadhyay Prithvijit","year":"2020","unstructured":"Prithvijit Chattopadhyay, Yogesh Balaji, and Judy Hoffman. 2020. Learning to balance specificity and invariance for in and out of domain generalization. In Proceedings of the 16th European Conference on Computer Vision\u2013ECCV 2020 Glasgow, UK, August 23\u201328, 2020. Springer, 301\u2013318."},{"issue":"12","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1038\/s41551-022-00929-8","article-title":"Fast and scalable search of whole-slide images via self-supervised deep learning","volume":"6","author":"Chen Chengkuan","year":"2022","unstructured":"Chengkuan Chen, Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Andrew J. Schaumberg, and Faisal Mahmood. 2022. Fast and scalable search of whole-slide images via self-supervised deep learning. Nature Biomedical Engineering 6, 12 (2022), 1420\u20131434.","journal-title":"Nature Biomedical Engineering"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01567"},{"issue":"3","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen Richard J.","year":"2024","unstructured":"Richard J. Chen, Tong Ding, Ming Y. Lu, and others. 2024. Towards a general-purpose foundation model for computational pathology. Nature Medicine 30, 3 (2024), 850\u2013862.","journal-title":"Nature Medicine"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100044"},{"issue":"4","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","article-title":"Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis","volume":"41","author":"Chen Richard J.","year":"2020","unstructured":"Richard J. Chen, Ming Y. Lu, Jingwen Wang, and others. 2020. Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Transactions on Medical Imaging 41, 4 (2020), 757\u2013770.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.07.004"},{"issue":"6","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen Richard J.","year":"2023","unstructured":"Richard J. Chen, Judy J. Wang, Drew F. K. Williamson, and others. 2023. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering 7, 6 (2023), 719\u2013742.","journal-title":"Nature Biomedical Engineering"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475496"},{"key":"e_1_3_2_37_2","first-page":"9164","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Chen Yang","year":"2021","unstructured":"Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, and Tao Mei. 2021. A style and semantic memory mechanism for domain generalization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9164\u20139173."},{"key":"e_1_3_2_38_2","unstructured":"Mathieu Chevalley Charlotte Bunne Andreas Krause and Stefan Bauer. 2022. Invariant causal mechanisms through distribution matching. arXiv:2206.11646. Retrieved from https:\/\/arxiv.org\/abs\/2206.11646"},{"key":"e_1_3_2_39_2","first-page":"420","volume-title":"Proceedings of the Lecture Notes in Computer Science","author":"Chikontwe Philip","year":"2022","unstructured":"Philip Chikontwe, Soo Jeong Nam, Heounjeong Go, Meejeong Kim, Hyun Jung Sung, and Sang Hyun Park. 2022. Feature re-calibration based multiple instance learning for whole slide image classification. In Proceedings of the Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, 420\u2013430. DOI:10.1007\/978-3-031-16434-7_41"},{"issue":"5","key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap Phillip","year":"2021","unstructured":"Phillip Chlap, Hang Min, Nym Vandenberg, Jason Dowling, Lois Holloway, and Annette Haworth. 2021. A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology 65, 5 (2021), 545\u2013563.","journal-title":"Journal of Medical Imaging and Radiation Oncology"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102580"},{"issue":"4","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1038\/s41374-020-00514-0","article-title":"Artificial intelligence and computational pathology","volume":"101","author":"Cui Miao","year":"2021","unstructured":"Miao Cui and David Y. Zhang. 2021. Artificial intelligence and computational pathology. Laboratory Investigation 101, 4 (2021), 412\u2013422.","journal-title":"Laboratory Investigation"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102485"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106095"},{"key":"e_1_3_2_45_2","first-page":"664","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Dawood Muhammad","year":"2021","unstructured":"Muhammad Dawood, Kim Branson, Nasir M. Rajpoot, and Fayyaz Minhas. 2021. Albrt: Cellular composition prediction in routine histology images. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 664\u2013673."},{"key":"e_1_3_2_46_2","volume-title":"Proceedings of the ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare","author":"Dawood Muhammad","year":"2023","unstructured":"Muhammad Dawood, Piotr Keller, and Fayyaz Minhas. 2023. Do tissue source sites leave identifiable signatures in whole slide images beyond staining?. In Proceedings of the ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare."},{"issue":"1","key":"e_1_3_2_47_2","doi-asserted-by":"crossref","first-page":"4884","DOI":"10.1038\/s41467-021-25221-2","article-title":"Deep learning-based transformation of H&E stained tissues into special stains","volume":"12","author":"Haan Kevin de","year":"2021","unstructured":"Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman, and others. 2021. Deep learning-based transformation of H&E stained tissues into special stains. Nature Communications 12, 1 (2021), 4884.","journal-title":"Nature Communications"},{"key":"e_1_3_2_48_2","volume-title":"Measurement in Medicine: A Practical Guide","author":"Vet Henrica C. W. De","year":"2011","unstructured":"Henrica C. W. De Vet, Caroline B. Terwee, Lidwine B. Mokkink, and Dirk L. Knol. 2011. Measurement in Medicine: A Practical Guide. Cambridge University Press."},{"key":"e_1_3_2_49_2","first-page":"102995","article-title":"SynCLay: Interactive synthesis of histology images from bespoke cellular layouts","author":"Deshpande Srijay","year":"2023","unstructured":"Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, and Nasir Rajpoot. 2023. SynCLay: Interactive synthesis of histology images from bespoke cellular layouts. Medical Image Analysis 91 (2023), 102995.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_2_50_2","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov and others. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929. Retrieved from https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"e_1_3_2_51_2","volume-title":"Proceedings of the NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications","author":"Dubois Yann","year":"2021","unstructured":"Yann Dubois, Yangjun Ruan, and Chris J. Maddison. 2021. Optimal representations for covariate shifts. In Proceedings of the NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications."},{"key":"e_1_3_2_52_2","first-page":"17340","article-title":"Probable domain generalization via quantile risk minimization","volume":"35","author":"Eastwood Cian","year":"2022","unstructured":"Cian Eastwood, Alexander Robey, Shashank Singh, and others. 2022. Probable domain generalization via quantile risk minimization. Advances in Neural Information Processing Systems 35 (2022), 17340\u201317358.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1021\/pr501254j","article-title":"The CPTAC data portal: A resource for cancer proteomics research","volume":"14","author":"Edwards Nathan J.","year":"2015","unstructured":"Nathan J. Edwards, Mauricio Oberti, Ratna R. Thangudu, Shuang Cai, Peter B. McGarvey, Shine Jacob, Subha Madhavan, and Karen A. Ketchum. 2015. The CPTAC data portal: A resource for cancer proteomics research. Journal of Proteome Research 14, 6 (2015), 2707\u20132713.","journal-title":"Journal of Proteome Research"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"e_1_3_2_55_2","first-page":"19955","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Eisenmann Matthias","year":"2023","unstructured":"Matthias Eisenmann, Annika Reinke, Vivienn Weru, and others. 2023. Why is the winner the best?. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 19955\u201319966."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2022.100006"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_57"},{"key":"e_1_3_2_58_2","volume-title":"Proceedings of the Medical Imaging with Deep Learning","author":"Faryna Khrystyna","year":"2021","unstructured":"Khrystyna Faryna, Jeroen van der Laak, and Geert Litjens. 2021. Tailoring automated data augmentation to H&E-stained histopathology. In Proceedings of the Medical Imaging with Deep Learning."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100133"},{"key":"e_1_3_2_60_2","first-page":"40","volume-title":"Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD","author":"Fick Rutger H. J.","year":"2021","unstructured":"Rutger H. J. Fick, Alireza Moshayedi, Gauthier Roy, Jules Dedieu, St\u00e9phanie Petit, and Saima Ben Hadj. 2021. Domain-specific cycle-GAN augmentation improves domain generalizability for mitosis detection. In Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD. Springer, 40\u201347."},{"key":"e_1_3_2_61_2","first-page":"2023","article-title":"Scaling self-supervised learning for histopathology with masked image modeling","author":"Filiot Alexandre","year":"2023","unstructured":"Alexandre Filiot, Ridouane Ghermi, Antoine Olivier, and others. 2023. Scaling self-supervised learning for histopathology with masked image modeling. medRxiv (2023), 2023\u201307.","journal-title":"medRxiv"},{"key":"e_1_3_2_62_2","first-page":"2023","article-title":"Scaling self-supervised learning for histopathology with masked image modeling","author":"Filiot Alexandre","year":"2023","unstructured":"Alexandre Filiot, Ridouane Ghermi, Antoine Olivier, and others. 2023. Scaling self-supervised learning for histopathology with masked image modeling. medRxiv (2023), 2023\u201307.","journal-title":"medRxiv"},{"key":"e_1_3_2_63_2","first-page":"1126","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1126\u20131135."},{"issue":"12","key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1093\/bioinformatics\/btac315","article-title":"REET: Robustness evaluation and enhancement toolbox for computational pathology","volume":"38","author":"Foote Alex","year":"2022","unstructured":"Alex Foote, Amina Asif, Nasir Rajpoot, and Fayyaz Minhas. 2022. REET: Robustness evaluation and enhancement toolbox for computational pathology. Bioinformatics 38, 12 (2022), 3312\u20133314.","journal-title":"Bioinformatics"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2019.2899364"},{"key":"e_1_3_2_66_2","unstructured":"Jevgenij Gamper Navid Alemi Koohbanani Ksenija Benes and others. 2020. Pannuke dataset extension insights and baselines. arXiv:2003.10778. Retrieved from https:\/\/arxiv.org\/abs\/2003.10778"},{"issue":"1","key":"e_1_3_2_67_2","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, and others. 2016. Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17, 1 (2016), 2096\u20132030.","journal-title":"The Journal of Machine Learning Research"},{"issue":"10","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent advances in open set recognition: A survey","volume":"43","author":"Geng Chuanxing","year":"2020","unstructured":"Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. 2020. Recent advances in open set recognition: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 10 (2020), 3614\u20133631.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102566"},{"key":"e_1_3_2_70_2","volume-title":"Digital Image Processing","author":"Gonzalez Rafael C.","year":"2009","unstructured":"Rafael C. Gonzalez. 2009. Digital Image Processing. Pearson Education India."},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-017-9314-z"},{"issue":"11","key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow Ian","year":"2020","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and others. 2020. Generative adversarial networks. Communications of the ACM 63, 11 (2020), 139\u2013144.","journal-title":"Communications of the ACM"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.3013246"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/iccvw54120.2021.00082"},{"key":"e_1_3_2_75_2","unstructured":"Simon Graham Mostafa Jahanifar Quoc Dang Vu and others. 2021. Conic: Colon nuclei identification and counting challenge 2022. arXiv:2111.14485. Retrieved from https:\/\/arxiv.org\/abs\/2111.14485"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102685"},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"103047","DOI":"10.1016\/j.media.2023.103047","article-title":"CoNIC challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting","volume":"92","author":"Graham Simon","year":"2024","unstructured":"Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, and others. 2024. CoNIC challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical Image Analysis 92 (2024), 103047.","journal-title":"Medical Image Analysis"},{"issue":"1","key":"e_1_3_2_78_2","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research 13, 1 (2012), 723\u2013773.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3117407"},{"key":"e_1_3_2_80_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Gulrajani Ishaan","year":"2021","unstructured":"Ishaan Gulrajani and David Lopez-Paz. 2021. In search of lost domain generalization. In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=lQdXeXDoWtI"},{"issue":"6","key":"e_1_3_2_81_2","first-page":"1","article-title":"Kappa statistic is not satisfactory for assessing the extent of agreement between raters","volume":"1","author":"Gwet Kilem","year":"2002","unstructured":"Kilem Gwet. 2002. Kappa statistic is not satisfactory for assessing the extent of agreement between raters. Statistical Methods for Inter-rater Reliability Assessment 1, 6 (2002), 1\u20136.","journal-title":"Statistical Methods for Inter-rater Reliability Assessment"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102481"},{"key":"e_1_3_2_83_2","series-title":"Lecture Notes in Computer Science","first-page":"303","volume-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022.","author":"Haq Mohammad Minhazul","year":"2022","unstructured":"Mohammad Minhazul Haq and Junzhou Huang. 2022. Self-supervised pre-training for nuclei segmentation. In Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022.Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, and Shuo Li (Eds.), Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 303\u2013313. DOI:10.1007\/978-3-031-16434-7_30"},{"key":"e_1_3_2_84_2","first-page":"9729","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","year":"2020","unstructured":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9729\u20139738."},{"key":"e_1_3_2_85_2","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770\u2013778."},{"issue":"2","key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10994-020-05924-1","article-title":"Conditional variance penalties and domain shift robustness","volume":"110","author":"Heinze-Deml Christina","year":"2021","unstructured":"Christina Heinze-Deml and Nicolai Meinshausen. 2021. Conditional variance penalties and domain shift robustness. Machine Learning 110, 2 (2021), 303\u2013348.","journal-title":"Machine Learning"},{"key":"e_1_3_2_87_2","article-title":"Segmentation metric misinterpretations in bioimage analysis","author":"Hirling Dominik","year":"2023","unstructured":"Dominik Hirling, Ervin Tasnadi, Juan Caicedo, and others. 2023. Segmentation metric misinterpretations in bioimage analysis. Nature Methods 21, 2 (2023), 213\u2013216.","journal-title":"Nature Methods"},{"issue":"4","key":"e_1_3_2_88_2","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1177\/0192623321993425","article-title":"HistoNet: A deep learning-based model of normal histology","volume":"49","author":"Hoefling Holger","year":"2021","unstructured":"Holger Hoefling, Tobias Sing, Imtiaz Hossain, and others. 2021. HistoNet: A deep learning-based model of normal histology. Toxicologic Pathology 49, 4 (2021), 784\u2013797.","journal-title":"Toxicologic Pathology"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.4103\/jpi.jpi_49_19"},{"issue":"9","key":"e_1_3_2_90_2","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales Timothy","year":"2021","unstructured":"Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2021. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 5149\u20135169.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"e_1_3_2_91_2","first-page":"1316","article-title":"Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks","volume":"23","author":"Hu Bo","year":"2018","unstructured":"Bo Hu, Ye Tang, I. Eric, and others. 2018. Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks. IEEE Journal of Biomedical and Health Informatics 23, 3 (2018), 1316\u20131328.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_92_2","first-page":"292","volume-title":"Proceedings of the Uncertainty in Artificial Intelligence","author":"Hu Shoubo","year":"2020","unstructured":"Shoubo Hu, Kun Zhang, Zhitang Chen, and Laiwan Chan. 2020. Domain generalization via multidomain discriminant analysis. In Proceedings of the Uncertainty in Artificial Intelligence. PMLR, 292\u2013302."},{"key":"e_1_3_2_93_2","article-title":"A visual\u2013language foundation model for pathology image analysis using medical Twitter","author":"Huang Zhi","year":"2023","unstructured":"Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J. Montine, and James Zou. 2023. A visual\u2013language foundation model for pathology image analysis using medical Twitter. Nature Medicine 29, 9 (2023), 2307\u20132316.","journal-title":"Nature Medicine"},{"key":"e_1_3_2_94_2","first-page":"561","volume-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021","author":"Huang Ziwang","year":"2021","unstructured":"Ziwang Huang, Hua Chai, Ruoqi Wang, Haitao Wang, Yuedong Yang, and Hejun Wu. 2021. Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images. In Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021. Springer International Publishing, Cham, 561\u2013570. DOI:10.1007\/978-3-030-87237-3_54"},{"key":"e_1_3_2_95_2","first-page":"124","volume-title":"Proceedings of the 16th European Conference on Computer Vision\u2013ECCV 2020, Glasgow, UK, August 23\u201328, 2020","author":"Huang Zeyi","year":"2020","unstructured":"Zeyi Huang, Haohan Wang, Eric P. Xing, and Dong Huang. 2020. Self-challenging improves cross-domain generalization. In Proceedings of the 16th European Conference on Computer Vision\u2013ECCV 2020, Glasgow, UK, August 23\u201328, 2020. Springer, 124\u2013140."},{"issue":"3","key":"e_1_3_2_96_2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1111\/his.14837","article-title":"Improving mitotic cell counting accuracy and efficiency using phosphohistone-H3 (PHH3) antibody counterstained with haematoxylin and eosin as part of breast cancer grading","volume":"82","author":"Ibrahim Asmaa","year":"2023","unstructured":"Asmaa Ibrahim, Michael S. Toss, Shorouk Makhlouf, Islam M. Miligy, Fayyaz Minhas, and Emad A. Rakha. 2023. Improving mitotic cell counting accuracy and efficiency using phosphohistone-H3 (PHH3) antibody counterstained with haematoxylin and eosin as part of breast cancer grading. Histopathology 82, 3 (2023), 393\u2013406.","journal-title":"Histopathology"},{"key":"e_1_3_2_97_2","first-page":"322","volume-title":"Proceedings of the Medical Imaging with Deep Learning","author":"Ilse Maximilian","year":"2020","unstructured":"Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, and Max Welling. 2020. Diva: Domain invariant variational autoencoders. In Proceedings of the Medical Imaging with Deep Learning. PMLR, 322\u2013348."},{"key":"e_1_3_2_98_2","first-page":"294","volume-title":"Proceedings of the Pacific Symposium on Biocomputing Co-chairs","author":"Irshad Humayun","year":"2014","unstructured":"Humayun Irshad, Laleh Montaser-Kouhsari, Gail Waltz, and others. 2014. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: Evaluating experts, automated methods, and the crowd. In Proceedings of the Pacific Symposium on Biocomputing Co-chairs. World Scientific, 294\u2013305."},{"key":"e_1_3_2_99_2","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/978-3-030-97281-3_6","volume-title":"Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis","author":"Jahanifar Mostafa","year":"2022","unstructured":"Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin, and others. 2022. Stain-robust mitotic figure detection for the mitosis domain generalization challenge. In Proceedings of the Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. Marc Aubreville, David Zimmerer, and Mattias Heinrich (Eds.), Springer International Publishing, Cham, 48\u201352."},{"key":"e_1_3_2_100_2","doi-asserted-by":"crossref","first-page":"103132","DOI":"10.1016\/j.media.2024.103132","article-title":"Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures","volume":"94","author":"Jahanifar Mostafa","year":"2024","unstructured":"Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin, and others. 2024. Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures. Medical Image Analysis 94 (2024), 103132.","journal-title":"Medical Image Analysis"},{"issue":"1","key":"e_1_3_2_101_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/technologies9010002","article-title":"A survey on contrastive self-supervised learning","volume":"9","author":"Jaiswal Ashish","year":"2020","unstructured":"Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2020. A survey on contrastive self-supervised learning. Technologies 9, 1 (2020), 2.","journal-title":"Technologies"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14215424"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102104"},{"key":"e_1_3_2_104_2","unstructured":"Junguang Jiang Yang Shu Jianmin Wang and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv:2201.05867. Retrieved from https:\/\/arxiv.org\/abs\/2201.05867"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106883"},{"key":"e_1_3_2_106_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Jiang Xiang","year":"2018","unstructured":"Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, and Stan Matwin. 2018. Learning to learn with conditional class dependencies. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_107_2","unstructured":"Yiping Jiao Jeroen van der Laak Shadi Albarqouni and others. 2023. LYSTO: The lymphocyte assessment hackathon and benchmark dataset. arXiv:2301.06304. Retrieved from https:\/\/arxiv.org\/abs\/2301.06304"},{"key":"e_1_3_2_108_2","first-page":"1","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson Justin M.","year":"2019","unstructured":"Justin M. Johnson and Taghi M. Khoshgoftaar. 2019. Survey on deep learning with class imbalance. Journal of Big Data 6 (2019), 1\u201354.","journal-title":"Journal of Big Data"},{"key":"e_1_3_2_109_2","first-page":"3344","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201923)","author":"Kang Mingu","year":"2023","unstructured":"Mingu Kang, Heon Song, Seonwook Park, Donggeun Yoo, and S\u00e9rgio Pereira. 2023. Benchmarking self-supervised learning on diverse pathology datasets. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201923). 3344\u20133354."},{"issue":"1","key":"e_1_3_2_110_2","doi-asserted-by":"crossref","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","article-title":"Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study","volume":"16","author":"Kather Jakob Nikolas","year":"2019","unstructured":"Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, and others. 2019. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine 16, 1 (2019), e1002730.","journal-title":"PLoS Medicine"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2303294"},{"key":"e_1_3_2_112_2","first-page":"180","volume-title":"Proceedings of the VLDB","author":"Kifer Daniel","year":"2004","unstructured":"Daniel Kifer, Shai Ben-David, and Johannes Gehrke. 2004. Detecting change in data streams. In Proceedings of the VLDB. Toronto, Canada, 180\u2013191."},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00948"},{"key":"e_1_3_2_114_2","doi-asserted-by":"crossref","unstructured":"Alexander Kirillov Eric Mintun Nikhila Ravi and others. 2023. Segment anything. arXiv:2304.02643. Retrieved from https:\/\/arxiv.org\/abs\/2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-020-00327-9"},{"key":"e_1_3_2_116_2","first-page":"5637","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Koh Pang Wei","year":"2021","unstructured":"Pang Wei Koh, Shiori Sagawa, Henrik Marklund, and others. 2021. WILDS: A benchmark of in-the-wild distribution shifts. In Proceedings of the 38th International Conference on Machine Learning. PMLR, 5637\u20135664. Retrieved from https:\/\/proceedings.mlr.press\/v139\/koh21a.html"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2021.3056023"},{"key":"e_1_3_2_118_2","first-page":"221","volume-title":"Proceedings of the MICCAI Challenge on Mitosis Domain Generalization","author":"Kotte Sujatha","year":"2022","unstructured":"Sujatha Kotte, V. G. Saipradeep, Naveen Sivadasan, and others. 2022. A deep learning based ensemble model for generalized mitosis detection in H&E stained whole slide images. In Proceedings of the MICCAI Challenge on Mitosis Domain Generalization. Springer, 221\u2013225."},{"key":"e_1_3_2_119_2","unstructured":"Masanori Koyama and Shoichiro Yamaguchi. 2020. When is invariance useful in an out-of-distribution generalization problem? arXiv:2008.01883. Retrieved from https:\/\/arxiv.org\/abs\/2008.01883"},{"key":"e_1_3_2_120_2","first-page":"5815","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, and others. 2021. Out-of-distribution generalization via risk extrapolation (rex). In Proceedings of the International Conference on Machine Learning. PMLR, 5815\u20135826."},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2947628"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101849"},{"key":"e_1_3_2_123_2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fmed.2019.00162","article-title":"Learning domain-invariant representations of histological images","volume":"6","author":"Lafarge Maxime W.","year":"2019","unstructured":"Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, and Mitko Veta. 2019. Learning domain-invariant representations of histological images. Frontiers in Medicine 6 (2019), 162.","journal-title":"Frontiers in Medicine"},{"key":"e_1_3_2_124_2","doi-asserted-by":"crossref","first-page":"102474","DOI":"10.1016\/j.media.2022.102474","article-title":"Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology","volume":"79","author":"Laleh Narmin Ghaffari","year":"2022","unstructured":"Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, and others. 2022. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 79 (2022), 102474.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_2_125_2","first-page":"393","volume-title":"Proceedings of the Medical Imagibychkov2018deepng with Deep Learning","author":"Laves Max-Heinrich","year":"2020","unstructured":"Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, L\u00fcder A. Kahrs, and Tobias Ortmaier. 2020. Well-calibrated regression uncertainty in medical imaging with deep learning. In Proceedings of the Medical Imagibychkov2018deepng with Deep Learning. PMLR, 393\u2013412."},{"key":"e_1_3_2_126_2","first-page":"4304","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Lazard Tristan","year":"2023","unstructured":"Tristan Lazard, Marvin Lerousseau, Etienne Decenci\u00e8re, and Thomas Walter. 2023. Giga-SSL: Self-supervised learning for gigapixel images. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4304\u20134313."},{"key":"e_1_3_2_127_2","doi-asserted-by":"crossref","first-page":"102206","DOI":"10.1016\/j.media.2021.102206","article-title":"Joint categorical and ordinal learning for cancer grading in pathology images","volume":"73","author":"Vuong Trinh Thi Le","year":"2021","unstructured":"Trinh Thi Le Vuong, Kyungeun Kim, Boram Song, and Jin Tae Kwak. 2021. Joint categorical and ordinal learning for cancer grading in pathology images. Medical Image Analysis 73 (2021), 102206.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_2_128_2","unstructured":"Jeaung Lee Jeewoo Lim Keunho Byeon and Jin Tae Kwak. 2024. Benchmarking pathology foundation models: Adaptation strategies and scenarios. arXiv:2410.16038. Retrieved from https:\/\/arxiv.org\/abs\/2410.16038"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1145\/3587095"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1145\/3555803"},{"key":"e_1_3_2_131_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Li Da","year":"2018","unstructured":"Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_132_2","first-page":"5400","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Li Haoliang","year":"2018","unstructured":"Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. 2018. Domain generalization with adversarial feature learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5400\u20135409."},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","unstructured":"Johann Li Guangming Zhu Cong Hua and others. 2023. A systematic collection of medical image datasets for deep learning. ACM Computing Surveys 56 5 (2023) 1\u201351. DOI:10.1145\/3615862","DOI":"10.1145\/3615862"},{"key":"e_1_3_2_134_2","first-page":"8886","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Li Pan","year":"2021","unstructured":"Pan Li, Da Li, Wei Li, Shaogang Gong, Yanwei Fu, and Timothy M. Hospedales. 2021. A simple feature augmentation for domain generalization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8886\u20138895."},{"key":"e_1_3_2_135_2","first-page":"303","volume-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Li Xiangyu","year":"2022","unstructured":"Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, and Shuo Li. 2022. ULTRA: Uncertainty-aware label distribution learning for breast tumor cellularity assessment. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 303\u2013312."},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_38"},{"key":"e_1_3_2_138_2","article-title":"A survey of convolutional neural networks: analysis, applications, and prospects","author":"Li Zewen","year":"2021","unstructured":"Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. 2021. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems 33, 12 (2021), 6999\u20137019.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107268"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102655"},{"key":"e_1_3_2_141_2","article-title":"The latent doctor model for modeling inter-observer variability","author":"Linmans Jasper","year":"2023","unstructured":"Jasper Linmans, Emiel Hoogeboom, Jeroen van der Laak, and Geert Litjens. 2023. The latent doctor model for modeling inter-observer variability. IEEE Journal of Biomedical and Health Informatics 28, 1 (2023), 343\u2013354.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.09.012"},{"key":"e_1_3_2_143_2","first-page":"75","volume-title":"Proceedings of the Resource-Efficient Medical Image Analysis","author":"Liu Quan","year":"2022","unstructured":"Quan Liu, Can Cui, Ruining Deng, and others. 2022. Leverage supervised and self-supervised pretrain models for pathological survival analysis via a simple and low-cost joint representation tuning. In Proceedings of the Resource-Efficient Medical Image Analysis. Springer Nature Switzerland, Cham, 75\u201384. DOI:10.1007\/978-3-031-16876-5_8"},{"key":"e_1_3_2_144_2","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"Liu Xiaofeng","year":"2021","unstructured":"Xiaofeng Liu, Bo Hu, Linghao Jin, and others. 2021. Domain generalization under conditional and label shifts via variational bayesian inference. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, California. DOI:10.24963\/ijcai.2021\/122"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_26"},{"key":"e_1_3_2_146_2","doi-asserted-by":"crossref","first-page":"102486","DOI":"10.1016\/j.media.2022.102486","article-title":"SlideGraph+: Whole slide image level graphs to predict HER2 status in breast cancer","volume":"80","author":"Lu Wenqi","year":"2022","unstructured":"Wenqi Lu, Michael Toss, Muhammad Dawood, Emad Rakha, Nasir Rajpoot, and Fayyaz Minhas. 2022. SlideGraph+: Whole slide image level graphs to predict HER2 status in breast cancer. Medical Image Analysis 80 (2022), 102486.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_2_147_2","first-page":"1107","volume-title":"Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","author":"Macenko Marc","year":"2009","unstructured":"Marc Macenko, Marc Niethammer, James S. Marron, and others. 2009. A method for normalizing histology slides for quantitative analysis. In Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 1107\u20131110."},{"key":"e_1_3_2_148_2","doi-asserted-by":"crossref","unstructured":"Amirreza Mahbod Christine Polak Katharina Feldmann and others. 2023. NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-Stained histological images. arXiv:2308.01760. Retrieved from https:\/\/arxiv.org\/abs\/2308.01760","DOI":"10.1038\/s41597-024-03117-2"},{"key":"e_1_3_2_149_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104349"},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2019.2927182"},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100183"},{"key":"e_1_3_2_152_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102165"},{"key":"e_1_3_2_153_2","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: An experimental study","author":"Mazurowski Maciej A.","year":"2023","unstructured":"Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang. 2023. Segment anything model for medical image analysis: An experimental study. Medical Image Analysis 89 (2023), 102918.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_2_154_2","unstructured":"Leland McInnes John Healy and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426. Retrieved from https:\/\/arxiv.org\/abs\/1802.03426"},{"key":"e_1_3_2_155_2","first-page":"390","volume-title":"Proceedings of the Communications in Computer and Information Science","author":"Minhas Fayyaz","year":"2021","unstructured":"Fayyaz Minhas, Michael S. Toss, Noor ul Wahab, Emad Rakha, and Nasir M. Rajpoot. 2021. L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy Na\u00efve Breast Cancer patients. In Proceedings of the Communications in Computer and Information Science. Springer International Publishing, Cham, 390\u2013400. DOI:DOI:10.1007\/978-3-030-93733-1_27"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09784-7"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.06.019"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102640"},{"key":"e_1_3_2_159_2","first-page":"10","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Muandet Krikamol","year":"2013","unstructured":"Krikamol Muandet, David Balduzzi, and Bernhard Sch\u00f6lkopf. 2013. Domain generalization via invariant feature representation. In Proceedings of the International Conference on Machine Learning. PMLR, 10\u201318."},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00858"},{"issue":"6","key":"e_1_3_2_161_2","doi-asserted-by":"crossref","first-page":"e0000278","DOI":"10.1371\/journal.pdig.0000278","article-title":"Bias in artificial intelligence algorithms and recommendations for mitigation","volume":"2","author":"Nazer Lama H.","year":"2023","unstructured":"Lama H. Nazer, Razan Zatarah, Shai Waldrip, and others. 2023. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health 2, 6 (2023), e0000278.","journal-title":"PLOS Digital Health"},{"key":"e_1_3_2_162_2","unstructured":"Peter Neidlinger Omar S. M. El Nahhas Hannah Sophie Muti and others. 2024. Benchmarking foundation models as feature extractors for weakly-supervised computational pathology. arXiv:2408.15823. Retrieved from https:\/\/arxiv.org\/abs\/2408.15823"},{"key":"e_1_3_2_163_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14102489"},{"key":"e_1_3_2_164_2","unstructured":"Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele and Bernhard Sch\u00f6lkopf. 2020. Learning explanations that are hard to vary. arXiv:2009.00329. Retrieved from https:\/\/arxiv.org\/abs\/2009.00329"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"issue":"1","key":"e_1_3_2_166_2","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1038\/s41597-022-01450-y","article-title":"HunCRC: Annotated pathological slides to enhance deep learning applications in colorectal cancer screening","volume":"9","author":"Pataki B\u00e1lint \u00c1rmin","year":"2022","unstructured":"B\u00e1lint \u00c1rmin Pataki, Alex Olar, Dezs\u0151 Ribli, and others. 2022. HunCRC: Annotated pathological slides to enhance deep learning applications in colorectal cancer screening. Scientific Data 9, 1 (2022), 370.","journal-title":"Scientific Data"},{"key":"e_1_3_2_167_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.05.010"},{"key":"e_1_3_2_168_2","first-page":"1256","article-title":"Gradient starvation: A learning proclivity in neural networks","volume":"34","author":"Pezeshki Mohammad","year":"2021","unstructured":"Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, and Guillaume Lajoie. 2021. Gradient starvation: A learning proclivity in neural networks. Advances in Neural Information Processing Systems 34 (2021), 1256\u20131272.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_169_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/s43856-022-00186-5","article-title":"TIAToolbox as an end-to-end library for advanced tissue image analytics","volume":"2","author":"Pocock Johnathan","year":"2022","unstructured":"Johnathan Pocock, Simon Graham, Quoc Dang Vu, and others. 2022. TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications Medicine 2, 1 (2022), 120.","journal-title":"Communications Medicine"},{"key":"e_1_3_2_170_2","volume-title":"Proceedings of the 19th European Congress on Digital Pathology (ECDP\u201923)","author":"Pohjonen Joona","year":"2023","unstructured":"Joona Pohjonen and others. 2023. HistoEncoder: Building a foundation model for histopathology. In Proceedings of the 19th European Congress on Digital Pathology (ECDP\u201923). Budapest, Hungary."},{"key":"e_1_3_2_171_2","unstructured":"Joona Pohjonen Carolin St\u00fcrenberg Atte F\u00f6hr Esa Pitk\u00e4nen Antti Rannikko and Tuomas Mirtti. 2022. Exposing and addressing the fragility of neural networks in digital pathology. arXiv:2206.15274. Retrieved from https:\/\/arxiv.org\/abs\/2206.15274"},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1145\/3234150"},{"issue":"7","key":"e_1_3_2_173_2","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1038\/s42256-020-0197-y","article-title":"Causal inference and counterfactual prediction in machine learning for actionable healthcare","volume":"2","author":"Prosperi Mattia","year":"2020","unstructured":"Mattia Prosperi, Yi Guo, Matt Sperrin, and others. 2020. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence 2, 7 (2020), 369\u2013375.","journal-title":"Nature Machine Intelligence"},{"key":"e_1_3_2_174_2","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/9780262170055.001.0001","volume-title":"Dataset Shift in Machine Learning","author":"Quinonero-Candela Joaquin","year":"2008","unstructured":"Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence. 2008. Dataset Shift in Machine Learning. MIT Press."},{"key":"e_1_3_2_175_2","first-page":"242","volume-title":"Proceedings of the 27th Conference on Medical Image Understanding and Analysis 2023","author":"Raza Manahil","year":"2023","unstructured":"Manahil Raza, Saad Bashir, Talha Qaiser, and others. 2023. Stain-invariant representation for tissue classification in histology images. In Proceedings of the 27th Conference on Medical Image Understanding and Analysis 2023. 242."},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.1109\/38.946629"},{"key":"e_1_3_2_177_2","unstructured":"Mart van Rijthoven and Others. 2022. TiGER: Tumour Infiltrating Lymphocytes in Breast Cancer. Retrieved August 15 2023 from https:\/\/tiger.grand-challenge.org\/"},{"key":"e_1_3_2_178_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.02.009"},{"key":"e_1_3_2_179_2","article-title":"MITOS and ATYPIA-Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images. IPAL, Agency Sci, Technol Res Inst Infocom Res","author":"Roux L.","year":"2014","unstructured":"L. Roux, D. Racoceanu, F. Capron, and others. 2014. MITOS and ATYPIA-Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images. IPAL, Agency Sci, Technol Res Inst Infocom Res. Technol. Res. Inst. Infocom Res., Singapore, Tech. Rep 1 (2014), 1\u20138.","journal-title":"Technol. Res. Inst. Infocom Res., Singapore, Tech. Rep"},{"issue":"4","key":"e_1_3_2_180_2","first-page":"291","article-title":"Quantification of histochemical staining by color deconvolution","volume":"23","author":"Ruifrok Arnout C.","year":"2001","unstructured":"Arnout C. Ruifrok, Dennis A. Johnston, and others. 2001. Quantification of histochemical staining by color deconvolution. Analytical and Quantitative Cytology and Histology 23, 4 (2001), 291\u2013299.","journal-title":"Analytical and Quantitative Cytology and Histology"},{"key":"e_1_3_2_181_2","first-page":"136","volume-title":"Proceedings of the MICCAI Workshop on Domain Adaptation and Representation Transfer","author":"Sadafi Ario","year":"2023","unstructured":"Ario Sadafi, Raheleh Salehi, Armin Gruber, and others. 2023. A continual learning approach for cross-domain white blood cell classification. In Proceedings of the MICCAI Workshop on Domain Adaptation and Representation Transfer. Springer, 136\u2013146."},{"key":"e_1_3_2_182_2","unstructured":"Shiori Sagawa Pang Wei Koh Tatsunori B. Hashimoto and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv:1911.08731. Retrieved from https:\/\/arxiv.org\/abs\/1911.08731"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510413"},{"issue":"5","key":"e_1_3_2_184_2","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Toward causal representation learning","volume":"109","author":"Sch\u00f6lkopf Bernhard","year":"2021","unstructured":"Bernhard Sch\u00f6lkopf, Francesco Locatello, Stefan Bauer, and others. 2021. Toward causal representation learning. Proceedings of the IEEE 109, 5 (2021), 612\u2013634.","journal-title":"Proceedings of the IEEE"},{"key":"e_1_3_2_185_2","unstructured":"Soroosh Shahtalebi Jean-Christophe Gagnon-Audet Touraj Laleh Mojtaba Faramarzi Kartik Ahuja and Irina Rish. 2021. Sand-mask: An enhanced gradient masking strategy for the discovery of invariances in domain generalization. arXiv:2106.02266. Retrieved from https:\/\/arxiv.org\/abs\/2106.02266"},{"key":"e_1_3_2_186_2","unstructured":"Fereshteh Shakeri Malik Boudiaf Sina Mohammadi and others. 2022. FHIST: A benchmark for few-shot classification of histological images. arXiv:2206.00092. Retrieved from https:\/\/arxiv.org\/abs\/2206.00092"},{"key":"e_1_3_2_187_2","unstructured":"Adam Shephard Mostafa Jahanifar Ruoyu Wang and others. 2022. TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge. arXiv:2206.11943. Retrieved from https:\/\/arxiv.org\/abs\/2206.11943"},{"key":"e_1_3_2_188_2","unstructured":"Adam J. Shephard Raja Muhammad Saad Bashir Hanya Mahmood and others. 2023. A fully automated and explainable algorithm for the prediction of malignant transformation in oral epithelial dysplasia. arXiv:2307.03757. Retrieved from https:\/\/arxiv.org\/abs\/2307.03757"},{"key":"e_1_3_2_189_2","unstructured":"Paras Sheth Raha Moraffah K. Sel\u00e7uk Candan Adrienne Raglin and Huan Liu. 2022. Domain generalization\u2013a causal perspective. arXiv:2209.15177. Retrieved from https:\/\/arxiv.org\/abs\/2209.15177"},{"key":"e_1_3_2_190_2","first-page":"9624","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Shu Yang","year":"2021","unstructured":"Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, and Mingsheng Long. 2021. Open domain generalization with domain-augmented meta-learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9624\u20139633."},{"issue":"1","key":"e_1_3_2_191_2","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1038\/s41598-023-33348-z","article-title":"Generalization of vision pre-trained models for histopathology","volume":"13","author":"Sikaroudi Milad","year":"2023","unstructured":"Milad Sikaroudi, Maryam Hosseini, Ricardo Gonzalez, Shahryar Rahnamayan, and H. R. Tizhoosh. 2023. Generalization of vision pre-trained models for histopathology. Scientific Reports 13, 1 (2023), 6065.","journal-title":"Scientific Reports"},{"key":"e_1_3_2_192_2","volume-title":"Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201922)","author":"Sikaroudi Milad","year":"2022","unstructured":"Milad Sikaroudi, Shahryar Rahnamayan, and H. R. Tizhoosh. 2022. Hospital-agnostic image representation learning in digital pathology. In Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201922). IEEE. DOI:10.1109\/embc48229.2022.9871198"},{"key":"e_1_3_2_193_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2022.102104"},{"key":"e_1_3_2_194_2","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/gutjnl-2019-319866","article-title":"Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning","volume":"70","author":"Sirinukunwattana Korsuk","year":"2020","unstructured":"Korsuk Sirinukunwattana, Enric Domingo, Susan D. Richman, and others. 2020. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut 70 (2020), 544\u2013554.","journal-title":"Gut"},{"key":"e_1_3_2_195_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102121"},{"key":"e_1_3_2_196_2","doi-asserted-by":"publisher","DOI":"10.1038\/s44222-023-00096-8"},{"key":"e_1_3_2_197_2","article-title":"Learning from noisy labels with deep neural networks: A survey","author":"Song Hwanjun","year":"2022","unstructured":"Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. 2022. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems 34, 11 (2022), 8135\u20138153.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_198_2","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: A survey","volume":"67","author":"Srinidhi Chetan L.","year":"2021","unstructured":"Chetan L. Srinidhi, Ozan Ciga, and Anne L. Martel. 2021. Deep neural network models for computational histopathology: A survey. Medical Image Analysis 67 (2021), 101813.","journal-title":"Medical Image Analysis"},{"issue":"1","key":"e_1_3_2_199_2","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929\u20131958.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_200_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2020.3032060"},{"key":"e_1_3_2_201_2","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s10463-008-0197-x","article-title":"Direct importance estimation for covariate shift adaptation","volume":"60","author":"Sugiyama Masashi","year":"2008","unstructured":"Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul Von B\u00fcnau, and Motoaki Kawanabe. 2008. Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics 60 (2008), 699\u2013746.","journal-title":"Annals of the Institute of Statistical Mathematics"},{"key":"e_1_3_2_202_2","first-page":"443","volume-title":"Proceedings of the Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8\u201310 and 15\u201316, 2016, Part III 14","author":"Sun Baochen","year":"2016","unstructured":"Baochen Sun and Kate Saenko. 2016. Deep coral: Correlation alignment for deep domain adaptation. In Proceedings of the Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8\u201310 and 15\u201316, 2016, Part III 14. Springer, 443\u2013450."},{"key":"e_1_3_2_203_2","volume-title":"Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW\u201921)","author":"Tang Sheyang","year":"2021","unstructured":"Sheyang Tang, Mahdi S. Hosseini, Lina Chen, and others. 2021. Probeable DARTS with application to computational pathology. In Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW\u201921). IEEE. DOI:10.1109\/iccvw54120.2021.00070"},{"key":"e_1_3_2_204_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820199"},{"key":"e_1_3_2_205_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101544"},{"key":"e_1_3_2_206_2","first-page":"1405","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Tran Luan","year":"2017","unstructured":"Luan Tran, Xiaoming Liu, Jiayu Zhou, and Rong Jin. 2017. Missing modalities imputation via cascaded residual autoencoder. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1405\u20131414."},{"key":"e_1_3_2_207_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16434-7_10"},{"issue":"8","key":"e_1_3_2_208_2","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","article-title":"Structure-preserving color normalization and sparse stain separation for histological images","volume":"35","author":"Vahadane Abhishek","year":"2016","unstructured":"Abhishek Vahadane, Tingying Peng, Amit Sethi, and others. 2016. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Transactions on Medical Imaging 35, 8 (2016), 1962\u20131971.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"e_1_3_2_209_2","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: The path to the clinic","volume":"27","author":"Laak Jeroen Van der","year":"2021","unstructured":"Jeroen Van der Laak, Geert Litjens, and Francesco Ciompi. 2021. Deep learning in histopathology: The path to the clinic. Nature Medicine 27, 5 (2021), 775\u2013784.","journal-title":"Nature Medicine"},{"issue":"11","key":"e_1_3_2_210_2","article-title":"Visualizing data using t-SNE.","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_211_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"e_1_3_2_212_2","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik Vladimir","year":"1999","unstructured":"Vladimir Vapnik. 1999. The Nature of Statistical Learning Theory. Springer Science and Business Media."},{"issue":"1","key":"e_1_3_2_213_2","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: Methodological failures and recommendations for the future","volume":"5","author":"Varoquaux Ga\u00ebl","year":"2022","unstructured":"Ga\u00ebl Varoquaux and Veronika Cheplygina. 2022. Machine learning for medical imaging: Methodological failures and recommendations for the future. NPJ Digital Medicine 5, 1 (2022), 48.","journal-title":"NPJ Digital Medicine"},{"key":"e_1_3_2_214_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.005"},{"key":"e_1_3_2_215_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102420"},{"key":"e_1_3_2_216_2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/978-3-030-00934-2_24","volume-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018.","author":"Veeling Bastiaan S.","year":"2018","unstructured":"Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. 2018. Rotation equivariant CNNs for digital pathology. In Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018.Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-L\u00f3pez, and Gabor Fichtinger (Eds.), Lecture Notes in Computer Science, Springer International Publishing, Cham, 210\u2013218. DOI:10.1007\/978-3-030-00934-2_24"},{"key":"e_1_3_2_217_2","doi-asserted-by":"crossref","DOI":"10.1002\/path.6163","article-title":"Computational pathology in cancer diagnosis, prognosis, and prediction\u2013present day and prospects","author":"Verghese Gregory","year":"2023","unstructured":"Gregory Verghese, Jochen K. Lennerz, Danny Ruta, and others. 2023. Computational pathology in cancer diagnosis, prognosis, and prediction\u2013present day and prospects. The Journal of Pathology 260, 5 (2023), 551\u2013563.","journal-title":"The Journal of Pathology"},{"key":"e_1_3_2_218_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3085712"},{"key":"e_1_3_2_219_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.02.012"},{"key":"e_1_3_2_220_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","author":"Vorontsov Eugene","year":"2024","unstructured":"Eugene Vorontsov, Alican Bozkurt, Adam Casson, and others. 2024. A foundation model for clinical-grade computational pathology and rare cancers detection. Nature Medicine 30, 10 (2024), 2924\u20132935.","journal-title":"Nature Medicine"},{"key":"e_1_3_2_221_2","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2019.00053"},{"key":"e_1_3_2_222_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21014-3_26"},{"key":"e_1_3_2_223_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102743"},{"key":"e_1_3_2_224_2","first-page":"543","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Vuong Trinh Thi Le","year":"2022","unstructured":"Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar, Simon Graham, Jin Tae Kwak, and Nasir Rajpoot. 2022. IMPaSh: A novel domain-shift resistant representation for colorectal cancer tissue classification. In Proceedings of the European Conference on Computer Vision. Springer, 543\u2013555."},{"key":"e_1_3_2_225_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16434-7_2"},{"key":"e_1_3_2_226_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_25"},{"issue":"2","key":"e_1_3_2_227_2","first-page":"116","article-title":"Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations","volume":"8","author":"Wahab Noorul","year":"2022","unstructured":"Noorul Wahab, Islam M. Miligy, Katherine Dodd, and others. 2022. Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations. The Journal of Pathology: Clinical Research 8, 2 (2022), 116\u2013128.","journal-title":"The Journal of Pathology: Clinical Research"},{"key":"e_1_3_2_228_2","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"Wang Jindong","year":"2021","unstructured":"Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, and Tao Qin. 2021. Generalizing to unseen domains: A survey on domain generalization. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, California. DOI:10.24963\/ijcai.2021\/628"},{"key":"e_1_3_2_229_2","unstructured":"Jindong Wang and Wang Lu. 2022. DeepDG: Deep Domain Generalization Toolkit. Retrieved July 31 2023 from https:\/\/github.com\/jindongwang\/transferlearning\/tree\/master\/code\/DeepDG"},{"issue":"2","key":"e_1_3_2_230_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3293318","article-title":"A survey of zero-shot learning: Settings, methods, and applications","volume":"10","author":"Wang Wei","year":"2019","unstructured":"Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao. 2019. A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 1\u201337.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_231_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559"},{"key":"e_1_3_2_232_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102703"},{"issue":"10","key":"e_1_3_2_233_2","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein John N.","year":"2013","unstructured":"John N. Weinstein, Eric A. Collisson, Gordon B. Mills, and others. 2013. The cancer genome atlas pan-cancer analysis project. Nature Genetics 45, 10 (2013), 1113\u20131120.","journal-title":"Nature Genetics"},{"key":"e_1_3_2_234_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"e_1_3_2_235_2","article-title":"A whole-slide foundation model for digital pathology from real-world data","author":"Xu Hanwen","year":"2024","unstructured":"Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, and others. 2024. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 8015 (2024), 181\u2013188.","journal-title":"Nature"},{"key":"e_1_3_2_236_2","article-title":"Vision transformers for computational histopathology","author":"Xu Hongming","year":"2023","unstructured":"Hongming Xu, Qi Xu, Fengyu Cong, and others. 2023. Vision transformers for computational histopathology. IEEE Reviews in Biomedical Engineering 17 (2023), 63\u201379.","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"e_1_3_2_237_2","unstructured":"Yilun Xu and Tommi Jaakkola. 2021. Learning representations that support robust transfer of predictors. arXiv:2110.09940. Retrieved from https:\/\/arxiv.org\/abs\/2110.09940"},{"key":"e_1_3_2_238_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101816"},{"key":"e_1_3_2_239_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2021.3101985"},{"key":"e_1_3_2_240_2","unstructured":"Shen Yan Huan Song Nanxiang Li Lincan Zou and Liu Ren. 2020. Improve unsupervised domain adaptation with mixup training. arXiv:2001.00677. Retrieved from https:\/\/arxiv.org\/abs\/2001.00677"},{"key":"e_1_3_2_241_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102539"},{"key":"e_1_3_2_242_2","first-page":"11842","volume-title":"Proceedings of the 38th International Conference on Machine Learning.Proceedings of Machine Learning Research","volume":"139","author":"Yang Yuzhe","year":"2021","unstructured":"Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, and Dina Katabi. 2021. Delving into deep imbalanced regression. In Proceedings of the 38th International Conference on Machine Learning.Marina Meila and Tong Zhang (Eds.), Proceedings of Machine Learning Research, Vol. 139, PMLR, 11842\u201311851. Retrieved from https:\/\/proceedings.mlr.press\/v139\/yang21m.html"},{"key":"e_1_3_2_243_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00696"},{"key":"e_1_3_2_244_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107441"},{"key":"e_1_3_2_245_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102748"},{"key":"e_1_3_2_246_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88210-5_27"},{"key":"e_1_3_2_247_2","first-page":"1","volume-title":"Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI\u201923)","author":"Zaffar Imaad","year":"2023","unstructured":"Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot, and Faisal Mahmood. 2023. Embedding space augmentation for weakly supervised learning in whole-slide images. In Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI\u201923). IEEE, 1\u20134."},{"key":"e_1_3_2_248_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103071"},{"key":"e_1_3_2_249_2","first-page":"288","volume-title":"Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021. Strasbourg, France, September 27\u2013October 1, 2021","author":"Zamanitajeddin Neda","year":"2021","unstructured":"Neda Zamanitajeddin, Mostafa Jahanifar, and Nasir Rajpoot. 2021. Cells are actors: Social network analysis with classical ml for sota histology image classification. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021. Strasbourg, France, September 27\u2013October 1, 2021. Springer, 288\u2013298."},{"key":"e_1_3_2_250_2","unstructured":"Neda Zamanitajeddin Mostafa Jahanifar Kesi Xu Fouzia Siraj and Nasir Rajpoot. 2024. Benchmarking domain generalization algorithms in computational pathology. arXiv:2409.17063. Retrieved from https:\/\/arxiv.org\/abs\/2409.17063"},{"key":"e_1_3_2_251_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16760-7_11"},{"key":"e_1_3_2_252_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_253_2","first-page":"10957","article-title":"Quantifying and improving transferability in domain generalization","volume":"34","author":"Zhang Guojun","year":"2021","unstructured":"Guojun Zhang, Han Zhao, Yaoliang Yu, and Pascal Poupart. 2021. Quantifying and improving transferability in domain generalization. Advances in Neural Information Processing Systems 34 (2021), 10957\u201310970.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_254_2","doi-asserted-by":"publisher","DOI":"10.1145\/3291124"},{"key":"e_1_3_2_255_2","first-page":"23664","article-title":"Adaptive risk minimization: Learning to adapt to domain shift","volume":"34","author":"Zhang Marvin","year":"2021","unstructured":"Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn. 2021. Adaptive risk minimization: Learning to adapt to domain shift. Advances in Neural Information Processing Systems 34 (2021), 23664\u201323678.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_256_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102415"},{"key":"e_1_3_2_257_2","first-page":"242","volume-title":"Proceedings of the Lecture Notes in Computer Science","author":"Zhang Yunlong","year":"2022","unstructured":"Yunlong Zhang, Yuxuan Sun, Honglin Li, Sunyi Zheng, Chenglu Zhu, and Lin Yang. 2022. Benchmarking the robustness of deep neural networks to common corruptions in digital pathology. In Proceedings of the Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, 242\u2013252. DOI:10.1007\/978-3-031-16434-7_24"},{"key":"e_1_3_2_258_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.01.008"},{"key":"e_1_3_2_259_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"e_1_3_2_260_2","doi-asserted-by":"crossref","first-page":"8008","DOI":"10.1109\/TIP.2021.3112012","article-title":"Domain adaptive ensemble learning","volume":"30","author":"Zhou Kaiyang","year":"2021","unstructured":"Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2021. Domain adaptive ensemble learning. IEEE Transactions on Image Processing 30 (2021), 8008\u20138018.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_261_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_262_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-86540-4"},{"key":"e_1_3_2_263_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhu Ronghang","year":"2022","unstructured":"Ronghang Zhu and Sheng Li. 2022. Crossmatch: Cross-classifier consistency regularization for open-set single domain generalization. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_264_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580218"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3724391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T12:21:50Z","timestamp":1749903710000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3724391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,14]]},"references-count":263,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3724391"],"URL":"https:\/\/doi.org\/10.1145\/3724391","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,14]]},"assertion":[{"value":"2023-11-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}