@book{Lamport:Book:1989,
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@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
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 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
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 year=2015
}
@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {FirstName LastName},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and  FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = ICCV,
pages = {234--778},
year = 2005
}

@article{wagner2023transformer,
  title={Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study},
  author={Wagner, Sophia J and Reisenb{\"u}chler, Daniel and West, Nicholas P and Niehues, Jan Moritz and Zhu, Jiefu and Foersch, Sebastian and Veldhuizen, Gregory Patrick and Quirke, Philip and Grabsch, Heike I and van den Brandt, Piet A and others},
  journal={Cancer cell},
  volume={41},
  number={9},
  pages={1650--1661},
  year={2023},
  publisher={Elsevier}
}


@article{chen2024uni,
  title={Towards a General-Purpose Foundation Model for Computational Pathology},
  author={Chen, Richard J and Ding, Tong and Lu, Ming Y and Williamson, Drew FK and Jaume, Guillaume and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Song, Andrew H and Shaban, Muhammad and others},
  journal={Nature Medicine},
  publisher={Nature Publishing Group},
  year={2024}
}
@article{BULTEN2021660,
title = {Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists},
journal = {Modern Pathology},
volume = {34},
number = {3},
pages = {660-671},
year = {2021},
issn = {0893-3952},
doi = {https://doi.org/10.1038/s41379-020-0640-y},
url = {https://www.sciencedirect.com/science/article/pii/S0893395222006652},
author = {Wouter Bulten and Maschenka Balkenhol and Jean-Joël Awoumou Belinga and Américo Brilhante and Aslı Çakır and Lars Egevad and Martin Eklund and Xavier Farré and Katerina Geronatsiou and Vincent Molinié and Guilherme Pereira and Paromita Roy and Günter Saile and Paulo Salles and Ewout Schaafsma and Joëlle Tschui and Anne-Marie Vos and  {ISUP Pathology Imagebase Expert Panel} and Hester {van Boven} and Robert Vink and Jeroen {van der Laak} and Christina {Hulsbergen-van der Kaa} and Geert Litjens and Brett Delahunt and Hemamali Samaratunga and David J. Grignon and Andrew J. Evans and Daniel M.Berney and Chin-Chen Pan and Glen Kristiansen and James G. Kench and Jon Oxley and Katia R.M. Leite and Jesse K. McKenney and Peter A. Humphrey and Samson W. Fine and Toyonori Tsuzuki and Murali Varma and Ming Zhou and Eva Comperat and David G. Bostwick and Kenneth A. Iczkowski and Cristina Magi-Galluzzi and John R. Srigley and Hiroyuki Takahashi and Theo {van der Kwast}},
abstract = {The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.}
}
@misc{shao2025multipleinstancelearningmodels,
      title={Do Multiple Instance Learning Models Transfer?}, 
      author={Daniel Shao and Richard J. Chen and Andrew H. Song and Joel Runevic and Ming Y. Lu and Tong Ding and Faisal Mahmood},
      year={2025},
      eprint={2506.09022},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.09022}, 
}
@misc{lu2023visuallanguagefoundationmodelcomputational,
      title={Towards a Visual-Language Foundation Model for Computational Pathology}, 
      author={Ming Y. Lu and Bowen Chen and Drew F. K. Williamson and Richard J. Chen and Ivy Liang and Tong Ding and Guillaume Jaume and Igor Odintsov and Andrew Zhang and Long Phi Le and Georg Gerber and Anil V Parwani and Faisal Mahmood},
      year={2023},
      eprint={2307.12914},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2307.12914}, 
}


@inproceedings{koch2024dinobloom,
  title={DinoBloom: a foundation model for generalizable cell embeddings in hematology},
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  pages={520--530},
  year={2024},
  organization={Springer}
}


@article{lu2021clam,
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  publisher={Nature Publishing Group}
}

@inproceedings{li2021dsmil,
  title={Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning},
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  year={2021}
}

@inproceedings{shao2021transmil,
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  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{zaheer2017deepsets,
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  publisher={Curran Associates, Inc.}
}

@inproceedings{lee2019set,
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  pages={3744--3753},
  year={2019},
  organization={PMLR}
}

@inproceedings{dosovitskiy2021vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@article{hehr2023explainable,
  title={Explainable AI identifies diagnostic cells of genetic AML subtypes},
  author={Hehr, Matthias and Sadafi, Ario and Matek, Christian and Lienemann, Peter and Pohlkamp, Christian and Haferlach, Torsten and Spiekermann, Karsten and Marr, Carsten},
  journal={PLOS Digital Health},
  volume={2},
  number={3},
  pages={e0000187},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{sidhom2021deep,
  title={Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features},
  author={Sidhom, John-William and Siddarthan, Ingharan J and Lai, Bo-Shiun and Luo, Adam and Hambley, Bryan C and Bynum, Jennifer and Duffield, Amy S and Streiff, Michael B and Moliterno, Alison R and Imus, Philip and others},
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  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{zhang2025standardizing,
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  year={2025}
}

@article{jacobs1991moe,
  title   = {Adaptive Mixtures of Local Experts},
  author  = {Jacobs, Robert A and Jordan, Michael I and Nowlan, Steven J and Hinton, Geoffrey E},
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}

@inproceedings{shazeer2017moe,
  title     = {Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer},
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}

@article{fedus2021switch,
  title   = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity},
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  journal = {arXiv preprint arXiv:2101.03961},
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  url     = {https://arxiv.org/abs/2101.03961}
}

@article{lepikhin2020gshard,
  title   = {GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding},
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}

@inproceedings{ilse2018attention,
  title     = {Attention-based Deep Multiple Instance Learning},
  author    = {Ilse, Maximilian and Tomczak, Jakub and Welling, Max},
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  year      = {2018},
  publisher = {PMLR},
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}

@article{campanella2019clinical,
  title   = {Clinical-grade computational pathology using weakly supervised deep learning on whole slide images},
  author  = {Campanella, Gabriele and Hanna, Matthew G. and Geneslaw, Luke and Miraflor, Allen and Werneck Krauss Silva, Vitor and Busam, Klaus J. and Brogi, Edi and Reuter, Victor E. and Klimstra, David S. and Fuchs, Thomas J.},
  journal = {Nature Medicine},
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  publisher = {Nature Publishing Group},
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}

@article{abnar2020quantifying,
  title={Quantifying attention flow in transformers},
  author={Abnar, Samira and Zuidema, Willem},
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}
@misc{ding2024multimodalslidefoundationmodel,
      title={Multimodal Whole Slide Foundation Model for Pathology}, 
      author={Tong Ding and Sophia J. Wagner and Andrew H. Song and Richard J. Chen and Ming Y. Lu and Andrew Zhang and Anurag J. Vaidya and Guillaume Jaume and Muhammad Shaban and Ahrong Kim and Drew F. K. Williamson and Bowen Chen and Cristina Almagro-Perez and Paul Doucet and Sharifa Sahai and Chengkuan Chen and Daisuke Komura and Akihiro Kawabe and Shumpei Ishikawa and Georg Gerber and Tingying Peng and Long Phi Le and Faisal Mahmood},
      year={2024},
      eprint={2411.19666},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2411.19666}, 
}


@article{vaidya2025molecular,
  title={Molecular-driven Foundation Model for Oncologic Pathology},
  author={Vaidya, Anurag and Zhang, Andrew and Jaume, Guillaume and Song, Andrew H and Ding, Tong and Wagner, Sophia J and Lu, Ming Y and Doucet, Paul and Robertson, Harry and Almagro-Perez, Cristina and others},
  journal={arXiv preprint arXiv:2501.16652},
  year={2025}
}

@article{brancati2022bracs,
  title={Bracs: A dataset for breast carcinoma subtyping in h\&e histology images},
  author={Brancati, Nadia and Anniciello, Anna Maria and Pati, Pushpak and Riccio, Daniel and Scognamiglio, Giosu{\`e} and Jaume, Guillaume and De Pietro, Giuseppe and Di Bonito, Maurizio and Foncubierta, Antonio and Botti, Gerardo and others},
  journal={Database},
  volume={2022},
  pages={baac093},
  year={2022},
  publisher={Oxford University Press UK}
}

@article{dorrich2025multimodal,
  title={A multimodal dataset for precision oncology in head and neck cancer},
  author={D{\"o}rrich, Marion and Balk, Matthias and Heusinger, Tatjana and Beyer, Sandra and Mirbagheri, Hamed and Fischer, David J and Kanso, Hassan and Matek, Christian and Hartmann, Arndt and Iro, Heinrich and others},
  journal={Nature Communications},
  volume={16},
  number={1},
  pages={7163},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@dataset{oliveira2024imp,
  author       = {Oliveira, Sara P. and Montezuma, Diana and Moreira, Ana and Oliveira, Domingos and Neto, Pedro C. and Monteiro, Ana and Monteiro, João and Ribeiro, Liliana and Gonçalves, Sofia and Pinto, Isabel M. and Cardoso, Jaime S.},
  title        = {IMP Whole-Slide Images of Cervical Samples 2024},
  year         = {2024},
  publisher    = {INESC TEC},
  doi          = {10.25747/9028-C140},
  url          = {https://doi.org/10.25747/9028-C140},
  note         = {Data set}
}

@article{bulten2022artificial,
  title={Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge},
  author={Bulten, Wouter and Kartasalo, Kimmo and Chen, Po-Hsuan Cameron and Str{\"o}m, Peter and Pinckaers, Hans and Nagpal, Kunal and Cai, Yuannan and Steiner, David F and Van Boven, Hester and Vink, Robert and others},
  journal={Nature medicine},
  volume={28},
  number={1},
  pages={154--163},
  year={2022},
  publisher={Nature Publishing Group US New York}
}

@misc{CPTAC_OV_2020,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  year         = {2020},
  title        = {The Clinical Proteomic Tumor Analysis Consortium Ovarian Serous Cystadenocarcinoma Collection (CPTAC-OV) (Version 3)},
  note         = {The Cancer Imaging Archive, Data set}
}
@article{Dy2024,
  author    = {Dy, A. and Nguyen, N. J. and Meyer, J. and Dawe, M. and Shi, W. and Androutsos, D. and Fyles, A. and Liu, F. F. and Done, S. and Khademi, A.},
  title     = {AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer},
  journal   = {Scientific Reports},
  year      = {2024},
  volume    = {14},
  number    = {1},
  pages     = {1283},
  doi       = {10.1038/s41598-024-51723-2},
  pmid      = {38218973},
  pmcid     = {PMC10787826},
  publisher = {Nature Publishing Group},
  url       = {https://doi.org/10.1038/s41598-024-51723-2}
}
@article{steiner2018impact,
  title   = {Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer},
  author  = {Steiner, David F. and MacDonald, Robert and Liu, Yun and Truszkowski, Peter and Hipp, Jason D. and Gammage, Christopher and Thng, Florence and Peng, Lily and Stumpe, Martin C.},
  journal = {The American Journal of Surgical Pathology},
  volume  = {42},
  number  = {12},
  pages   = {1636--1646},
  year    = {2018},
  doi     = {10.1097/PAS.0000000000001151},
  publisher = {Lippincott Williams \& Wilkins},
  issn    = {0147-5185},
  pmid    = {30222528}
}

@InProceedings{Wu_2025_CVPR,
  author    = {Wu, Junxian and Chen, Minheng and Ke, Xinyi and Xun, Tianwang and 
               Jiang, Xiaoming and Zhou, Hongyu and Shao, Lizhi and Kong, Youyong},
  title     = {Learning Heterogeneous Tissues with Mixture of Experts for Gigapixel Whole Slide Images},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2025},
  pages     = {5144--5153}
}
@article{ellis2013clinical,
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}

@article{bilal2021weakly,
  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},
  author       = {Bilal, M. and Chen, R.~J. and van der Laak, J. and de Bruyn, T. and Litjens, G. and others},
  journal      = {The Lancet Digital Health},
  year         = {2021},
  volume       = {3},
  number       = {e8},
  pages        = {e763--e772},
  doi          = {10.1016/S2589-7500(21)00133-3}
}

@article{vorontsov2024foundation,
  title        = {A foundation model for clinical-grade computational pathology and rare cancers detection},
  author       = {Vorontsov, Eugene and Bozkurt, Alican and Casson, Adam and Shaikovski, George and Zelechowski, Michal and Severson, Kristen and Zimmermann, Eric and Hall, James and Tenenholtz, Neil and Fusi, Nicolo and Yang, Ellen and Mathieu, Philippe and van Eck, Alexander and Lee, Donghun and Viret, Julian and Robert, Eric and Wang, Yi Kan and Kunz, Jeremy D. and Lee, Matthew C.H. and Bernhard, Jan H. and Godrich, Ran A. and Oakley, Gerard and Millar, Ewan and Hanna, Matthew and Wen, Hannah and Retamero, Juan A. and Moye, William A. and Yousfi, Razik and Kanan, Christopher and Klimstra, David S. and Rothrock, Brandon and Liu, Siqi and Fuchs, Thomas J.},
  journal      = {Nature Medicine},
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  pages        = {2924--2935},
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}
@misc{riquelme2021scalingvisionsparsemixture,
      title={Scaling Vision with Sparse Mixture of Experts}, 
      author={Carlos Riquelme and Joan Puigcerver and Basil Mustafa and Maxim Neumann and Rodolphe Jenatton and André Susano Pinto and Daniel Keysers and Neil Houlsby},
      year={2021},
      eprint={2106.05974},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2106.05974}, 
}


@article{Li2023_histopath_tissueArea_CRCsurvival,
  title        = {A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images},
  author       = {Li, Yan-Jun and Chou, Hsin-Hung and Lin, Peng-Chan and Shen, Meng-Ru and Hsieh, Sun-Yuan and others},
  journal      = {Journal of Translational Medicine},
  year         = {2023},
  volume       = {21},
  number       = {1},
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}

@article{janowczyk2019histoqc,
  title     = {HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides},
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  year      = {2019},
  month     = apr,
  doi       = {10.1200/CCI.18.00157},
  pmid      = {30990737},
  pmcid     = {PMC6552675},
  publisher = {American Society of Clinical Oncology}
}

@misc{
nie2022densetosparse,
title={Dense-to-Sparse Gate for Mixture-of-Experts},
author={Xiaonan Nie and Shijie Cao and Xupeng Miao and Lingxiao Ma and Jilong Xue and Youshan Miao and Zichao Yang and Zhi Yang and Bin CUI},
year={2022},
url={https://openreview.net/forum?id=_4D8IVs7yO8}
}

@inbook{inbook_review,
author = {Ilse, Maximilian and Tomczak, Jakub and Welling, Max},
year = {2020},
month = {01},
pages = {521-546},
title = {Deep multiple instance learning for digital histopathology},
isbn = {9780128161760},
doi = {10.1016/B978-0-12-816176-0.00027-2}
}
@misc{keshvarikhojasteh2025quantitativeevaluationmultipleinstance,
      title={Quantitative Evaluation of Multiple Instance Learning Reliability For WSIs Classification}, 
      author={Hassan Keshvarikhojasteh},
      year={2025},
      eprint={2409.11110},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.11110}, 
}

@software{hoptimus1,
  author = {Bioptimus},
  title = {H-optimus-1},
  url = {https://huggingface.co/bioptimus/H-optimus-1},
  year = {2025},
}

@article{zimmermann2024virchow2,
  title={Virchow2: Scaling self-supervised mixed magnification models in pathology},
  author={Zimmermann, Eric and Vorontsov, Eugene and Viret, Julian and Casson, Adam and Zelechowski, Michal and Shaikovski, George and Tenenholtz, Neil and Hall, James and Klimstra, David and Yousfi, Razik and others},
  journal={arXiv preprint arXiv:2408.00738},
  year={2024}
}

@article{filiot2024phikon,
  title={Phikon-v2, a large and public feature extractor for biomarker prediction},
  author={Filiot, Alexandre and Jacob, Paul and Mac Kain, Alice and Saillard, Charlie},
  journal={arXiv preprint arXiv:2409.09173},
  year={2024}
}

@article{lu2024visual,
  title={A visual-language foundation model for computational pathology},
  author={Lu, Ming Y and Chen, Bowen and Williamson, Drew FK and Chen, Richard J and Liang, Ivy and Ding, Tong and Jaume, Guillaume and Odintsov, Igor and Le, Long Phi and Gerber, Georg and others},
  journal={Nature medicine},
  volume={30},
  number={3},
  pages={863--874},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@inproceedings{sadafi2020attention,
  title={Attention based multiple instance learning for classification of blood cell disorders},
  author={Sadafi, Ario and Makhro, Asya and Bogdanova, Anna and Navab, Nassir and Peng, Tingying and Albarqouni, Shadi and Marr, Carsten},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={246--256},
  year={2020},
  organization={Springer}
}

@article{dasdelen2026ai,
  title={{AI}-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort},
  author={Dasdelen, Muhammed Furkan and Kukuljan, Ivan and Lienemann, Peter and Ozlugedik, Fatih and Sadafi, Ario and Hehr, Matthias and Spiekermann, Karsten and Pohlkamp, Christian and Marr, Carsten},
  journal={Leukemia},
  pages={1--5},
  year={2026},
  publisher={Nature Publishing Group UK London}
}

@article{xu2021predicting,
  title={Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides},
  author={Xu, Feng and Zhu, Chuang and Tang, Wenqi and Wang, Ying and Zhang, Yu and Li, Jie and Jiang, Hongchuan and Shi, Zhongyue and Liu, Jun and Jin, Mulan},
  journal={Frontiers in oncology},
  volume={11},
  pages={759007},
  year={2021},
  publisher={Frontiers Media SA}
}

@article{lin2002divergence,
  title={Divergence measures based on the Shannon entropy},
  author={Lin, Jianhua},
  journal={IEEE Transactions on Information theory},
  volume={37},
  number={1},
  pages={145--151},
  year={2002},
  publisher={IEEE}
}

@dataset{cptac_ccrcc_2018,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  year         = {2018},
  title        = {The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC) (Version 14)},
  howpublished = {The Cancer Imaging Archive},
  doi          = {10.7937/K9/TCIA.2018.OBLAMN27},
  url          = {https://doi.org/10.7937/K9/TCIA.2018.OBLAMN27},
  note         = {[Dataset]}
}

@dataset{cptac_hnscc_2018,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  year         = {2018},
  title        = {The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 19)},
  howpublished = {The Cancer Imaging Archive},
  doi          = {10.7937/K9/TCIA.2018.UW45NH81},
  url          = {https://doi.org/10.7937/K9/TCIA.2018.UW45NH81},
  note         = {[Dataset]}
}

@dataset{cptac_lscc_2018,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  year         = {2018},
  title        = {The Clinical Proteomic Tumor Analysis Consortium Lung Squamous Cell Carcinoma Collection (CPTAC-LSCC) (Version 15)},
  howpublished = {The Cancer Imaging Archive},
  doi          = {10.7937/K9/TCIA.2018.6EMUB5L2},
  url          = {https://doi.org/10.7937/K9/TCIA.2018.6EMUB5L2},
  note         = {[Dataset]}
}

@dataset{cptac_ucec_2019,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  year         = {2019},
  title        = {The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC) (Version 13)},
  howpublished = {The Cancer Imaging Archive},
  doi          = {10.7937/K9/TCIA.2018.3R3JUISW},
  url          = {https://doi.org/10.7937/K9/TCIA.2018.3R3JUISW},
  note         = {[Dataset]}
}
@article{LI2025103561,
title = {M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis},
journal = {Medical Image Analysis},
volume = {103},
pages = {103561},
year = {2025},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2025.103561},
url = {https://www.sciencedirect.com/science/article/pii/S1361841525001082},
author = {Junyu Li and Ye Zhang and Wen Shu and Xiaobing Feng and Yingchun Wang and Pengju Yan and Xiaolin Li and Chulin Sha and Min He},
keywords = {Multiple instance learning, Multi-task learning, Whole slide image, Genetic mutation},
abstract = {Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers. However, existing MIL methods predominantly focus on single-task learning, resulting in not only overall low efficiency but also the overlook of inter-task relatedness. To address these issues, we proposed an adapted architecture of Multi-gate Mixture-of-experts with Multi-proxy for Multiple instance learning (M4), and applied this framework for simultaneous prediction of multiple genetic mutations from WSIs. The proposed M4 model has two main innovations: (1) adopting a multi-gate mixture-of-experts strategy for multiple genetic mutation simultaneous prediction on a single WSI; (2) introducing a multi-proxy CNN construction on the expert and gate networks to effectively and efficiently capture patch-patch interactions from WSI. Our model achieved significant improvements across five tested TCGA datasets in comparison to current state-of-the-art single-task methods. The code is available at: https://github.com/Bigyehahaha/M4.}
}