@article{clam,
  title={Data-efficient and weakly supervised computational pathology on whole-slide images},
  author={Lu, Ming Y and Williamson, Drew FK and Chen, Tiffany Y and Chen, Richard J and Barbieri, Matteo and Mahmood, Faisal},
  journal={Nature biomedical engineering},
  volume={5},
  number={6},
  pages={555--570},
  year={2021},
  publisher={Nature Publishing Group UK London}
}


@inproceedings{ilse2018attentionbaseddeepmultipleinstance,
  title={Attention-based deep multiple instance learning},
  author={Ilse, Maximilian and Tomczak, Jakub and Welling, Max},
  booktitle={International conference on machine learning},
  pages={2127--2136},
  year={2018},
  organization={PMLR}
}


@article{shao2021transmiltransformerbasedcorrelated,
  title={Transmil: Transformer based correlated multiple instance learning for whole slide image classification},
  author={Shao, Zhuchen and Bian, Hao and Chen, Yang and Wang, Yifeng and Zhang, Jian and Ji, Xiangyang and others},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={2136--2147},
  year={2021}
}


@inproceedings{li2021dualstreammultipleinstancelearning,
  title={Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning},
  author={Li, Bin and Li, Yin and Eliceiri, Kevin W},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={14318--14328},
  year={2021}
}

@inproceedings{zhang2022dtfdmildoubletierfeaturedistillation,
  title={Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification},
  author={Zhang, Hongrun and Meng, Yanda and Zhao, Yitian and Qiao, Yihong and Yang, Xiaoyun and Coupland, Sarah E and Zheng, Yalin},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={18802--18812},
  year={2022}
}

%

@article{javed2022additivemilintrinsicallyinterpretable,
  title={Additive mil: Intrinsically interpretable multiple instance learning for pathology},
  author={Javed, Syed Ashar and Juyal, Dinkar and Padigela, Harshith and Taylor-Weiner, Amaro and Yu, Limin and Prakash, Aaditya},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={20689--20702},
  year={2022}
}
 %

@article{early2024inherentlyinterpretabletimeseries,
  title={Inherently interpretable time series classification via multiple instance learning},
  author={Early, Joseph and Cheung, Gavin KC and Cutajar, Kurt and Xie, Hanting and Kandola, Jas and Twomey, Niall},
  journal={arXiv preprint arXiv:2311.10049},
  year={2023}
}

%

@article{hense2025xmilinsightfulexplanationsmultiple,
  title={XMIL: Insightful explanations for multiple instance learning in histopathology},
  author={Hense, Julius and Jamshidi Idaji, Mina and Eberle, Oliver and Schnake, Thomas and Dippel, Jonas and Ciernik, Laure and Buchstab, Oliver and Mock, Andreas and Klauschen, Frederick and M{\"u}ller, Klaus-Robert},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={8300--8328},
  year={2024}
}

%
@inproceedings{mhim-mil,
  title={Multiple instance learning framework with masked hard instance mining for whole slide image classification},
  author={Tang, Wenhao and Huang, Sheng and Zhang, Xiaoxian and Zhou, Fengtao and Zhang, Yi and Liu, Bo},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={4078--4087},
  year={2023}
}
%

@inproceedings{zhang2024attentionchallengingmultipleinstancelearning,
  title={Attention-challenging multiple instance learning for whole slide image classification},
  author={Zhang, Yunlong and Li, Honglin and Sun, Yunxuan and Zheng, Sunyi and Zhu, Chenglu and Yang, Lin},
  booktitle={European conference on computer vision},
  pages={125--143},
  year={2024},
  organization={Springer}
}
%
@article{vaswani2023attentionneed,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
%
@article{fourkioti2024camilcontextawaremultipleinstance,
  title={CAMIL: Context-aware multiple instance learning for cancer detection and subtyping in whole slide images},
  author={Fourkioti, Olga and De Vries, Matt and Jin, Chen and Alexander, Daniel C and Bakal, Chris},
  journal={arXiv preprint arXiv:2305.05314},
  year={2023}
}
%
@article{jang2024are,
  title={Are multiple instance learning algorithms learnable for instances?},
  author={Jang, Jaeseok and Kwon, Hyuk-Yoon},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={10575--10612},
  year={2024}
}
%

@inproceedings{xiong2021nystromformernystrombasedalgorithmapproximating,
  title={Nystr{\"o}mformer: A nystr{\"o}m-based algorithm for approximating self-attention},
  author={Xiong, Yunyang and Zeng, Zhanpeng and Chakraborty, Rudrasis and Tan, Mingxing and Fung, Glenn and Li, Yin and Singh, Vikas},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  pages={14138--14148},
  year={2021}
}
%
@article{revisiting-MIL,
  title={Revisiting multiple instance neural networks},
  author={Wang, Xinggang and Yan, Yongluan and Tang, Peng and Bai, Xiang and Liu, Wenyu},
  journal={Pattern recognition},
  volume={74},
  pages={15--24},
  year={2018},
  publisher={Elsevier}
}
%
@inproceedings{lin2023interventionalbagmultiinstancelearning,
  title={Interventional bag multi-instance learning on whole-slide pathological images},
  author={Lin, Tiancheng and Yu, Zhimiao and Hu, Hongyu and Xu, Yi and Chen, Chang-Wen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19830--19839},
  year={2023}
}
%

@inproceedings{catt,
  title={Causal ATTention Multiple Instance Learning for Whole Slide Image Classification},
  author={Wu, Xiaochun and Wang, Haitao and Wu, Hejun},
  booktitle={The 16th Asian Conference on Machine Learning (Conference Track)},
  year={2024}
}

%
@inproceedings{dtfd,
  title={Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification},
  author={Zhang, Hongrun and Meng, Yanda and Zhao, Yitian and Qiao, Yihong and Yang, Xiaoyun and Coupland, Sarah E and Zheng, Yalin},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={18802--18812},
  year={2022}
}

%
@inproceedings{gradCam,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}
%

@article{qu2022bidirectionalweaklysupervisedknowledge,
  title={Bi-directional weakly supervised knowledge distillation for whole slide image classification},
  author={Qu, Linhao and Wang, Manning and Song, Zhijian and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={15368--15381},
  year={2022}
}

%

@article{MIL,
  title={Solving the multiple instance problem with axis-parallel rectangles},
  author={Dietterich, Thomas G and Lathrop, Richard H and Lozano-P{\'e}rez, Tom{\'a}s},
  journal={Artificial intelligence},
  volume={89},
  number={1-2},
  pages={31--71},
  year={1997},
  publisher={Elsevier}
}

%
@inproceedings{rao2021counterfactualattentionlearningfinegrained,
  title={Counterfactual attention learning for fine-grained visual categorization and re-identification},
  author={Rao, Yongming and Chen, Guangyi and Lu, Jiwen and Zhou, Jie},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={1025--1034},
  year={2021}
}

%
@article{pearl_old,
  title={Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000},
  author={Neuberg, Leland Gerson},
  journal={Econometric Theory},
  volume={19},
  number={4},
  pages={675--685},
  year={2003},
  publisher={cambridge university press}
}

@book{pearl,
  title={Causal inference in statistics: A primer},
  author={Pearl, Judea and Glymour, Madelyn and Jewell, Nicholas P},
  year={2016},
  publisher={John Wiley \& Sons}
}
%
@book{bookofwhy,
  title={The book of why: the new science of cause and effect},
  author={Pearl, Judea and Mackenzie, Dana},
  year={2018},
  publisher={Basic books}
}
%
@article{wachter2018counterfactualexplanationsopeningblack,
  title={Counterfactual explanations without opening the black box: Automated decisions and the GDPR},
  author={Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  journal={Harv. JL \& Tech.},
  volume={31},
  pages={841},
  year={2017},
  publisher={HeinOnline}
}
%
@article{torchvision,
  title={TorchVision: PyTorch’s computer vision library},
  author={Maintainers, T and contributors, A},
  journal={GitHub repository},
  year={2016}
}
%
@inproceedings{ImageNet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}
%
@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 Song, Andrew H and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Shaban, Muhammad and others},
  journal={Nature medicine},
  volume={30},
  number={3},
  pages={850--862},
  year={2024},
  publisher={Nature Publishing Group US New York}
}
%
@article{shapley1953value,
  title={A value for n-person games},
  author={Shapley, Lloyd S and others},
  year={1953},
  publisher={Princeton University Press Princeton}
}
%
@article{adam2014method,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}
%
@article{early2022modelagnosticinterpretabilitymultiple,
  title={Model agnostic interpretability for multiple instance learning},
  author={Early, Joseph and Evers, Christine and Ramchurn, Sarvapali},
  journal={arXiv preprint arXiv:2201.11701},
  year={2022}
}
%

% TCGA
@article{tomczak2015review,
  title={Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge},
  author={Tomczak, Katarzyna and Czerwi{\'n}ska, Patrycja and Wiznerowicz, Maciej},
  journal={Contemporary Oncology/Wsp{\'o}{\l}czesna Onkologia},
  volume={2015},
  number={1},
  pages={68--77},
  year={2015},
  publisher={Termedia}
}
%

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

@article{abnar2020quantifying,
  title={Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?},
  author={Jacovi, Alon and Goldberg, Yoav},
  journal={arXiv preprint arXiv:2004.03685},
  year={2020}
}
%
@article{article_wsi_mil_1,
  title={Multiple instance classification: Review, taxonomy and comparative study},
  author={Amores, Jaume},
  journal={Artificial intelligence},
  volume={201},
  pages={81--105},
  year={2013},
  publisher={Elsevier}
}
%
@article{mil_maron1997framework,
  title={A framework for multiple-instance learning},
  author={Maron, Oded and Lozano-P{\'e}rez, Tom{\'a}s},
  journal={Advances in neural information processing systems},
  volume={10},
  year={1997}
}
%
@article{histo_mil_1,
  title={Deep learning for identifying metastatic breast cancer},
  author={Wang, Dayong and Khosla, Aditya and Gargeya, Rishab and Irshad, Humayun and Beck, Andrew H},
  journal={arXiv preprint arXiv:1606.05718},
  year={2016}
}
%

@article{histo_mil_2,
  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 Silva, Vitor Werneck Krauss and Busam, Klaus J and Brogi, Edi and Reuter, Victor E and Klimstra, David S and Fuchs, Thomas J},
  journal={Nature Medicine},
  volume={25},
  number={8},
  pages={1301--1309},
  year={2019},
  publisher={Nature Publishing Group}
}
%
@article{histo_mil_3,
  title={Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes},
  author={Diao, James A and Wang, Jason K and Chui, Wan Fung and Mountain, Victoria and Gullapally, Sai Chowdary and Srinivasan, Ramprakash and Mitchell, Richard N and Glass, Benjamin and Hoffman, Sara and Rao, Sudha K and others},
  journal={Nature Communications},
  volume={12},
  number={1},
  pages={1--15},
  year={2021},
  publisher={Nature Publishing Group}
}
%
@inproceedings{lin2024boosting,
  title={Boosting multiple instance learning models for whole slide image classification: A model-agnostic framework based on counterfactual inference},
  author={Lin, Weiping and Zhuang, Zhenfeng and Yu, Lequan and Wang, Liansheng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={4},
  pages={3477--3485},
  year={2024}
}
%
@inproceedings{histo_interp_1,
  title={Improving interpretability for computer-aided diagnosis tools on whole slide imaging with multiple instance learning and gradient-based explanations},
  author={Pirovano, Antoine and Heuberger, Hippolyte and Berlemont, Sylvain and Ladjal, Sa{\"\i}d and Bloch, Isabelle},
  booktitle={International Workshop on Interpretability of Machine Intelligence in Medical Image Computing},
  pages={43--53},
  year={2020},
  organization={Springer}
}
%
@article{histo_interp_2,
  title={Machine learning models for multiparametric glioma grading with quantitative result interpretations},
  author={Wang, Xiuying and Wang, Dingqian and Yao, Zhigang and Xin, Bowen and Wang, Bao and Lan, Chuanjin and Qin, Yejun and Xu, Shangchen and He, Dazhong and Liu, Yingchao},
  journal={Frontiers in neuroscience},
  volume={12},
  pages={1046},
  year={2019},
  publisher={Frontiers Media SA}
}
%
@incollection{AI_reliability,
  title={The (un) reliability of saliency methods},
  author={Kindermans, Pieter-Jan and Hooker, Sara and Adebayo, Julius and Alber, Maximilian and Sch{\"u}tt, Kristof T and D{\"a}hne, Sven and Erhan, Dumitru and Kim, Been},
  booktitle={Explainable AI: Interpreting, explaining and visualizing deep learning},
  pages={267--280},
  year={2019},
  publisher={Springer}
}
%
@article{adebayo2018sanity,
  title={Sanity checks for saliency maps},
  author={Adebayo, Julius and Gilmer, Justin and Muelly, Michael and Goodfellow, Ian and Hardt, Moritz and Kim, Been},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}
%
@inproceedings{molnar2020general,
  title={General pitfalls of model-agnostic interpretation methods for machine learning models},
  author={Molnar, Christoph and K{\"o}nig, Gunnar and Herbinger, Julia and Freiesleben, Timo and Dandl, Susanne and Scholbeck, Christian A and Casalicchio, Giuseppe and Grosse-Wentrup, Moritz and Bischl, Bernd},
  booktitle={International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers},
  pages={39--68},
  year={2020},
  organization={Springer}
}
%
@inproceedings{resnet,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
%
@article{van2022tractability,
  title={On the tractability of SHAP explanations},
  author={Van den Broeck, Guy and Lykov, Anton and Schleich, Maximilian and Suciu, Dan},
  journal={Journal of Artificial Intelligence Research},
  volume={74},
  pages={851--886},
  year={2022}
}

%
@article{lu2021ai,
  title={AI-based pathology predicts origins for cancers of unknown primary},
  author={Lu, Ming Y and Chen, Tiffany Y and Williamson, Drew FK and Zhao, Melissa and Shady, Maha and Lipkova, Jana and Mahmood, Faisal},
  journal={Nature},
  volume={594},
  number={7861},
  pages={106--110},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

%
@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{jain2019attention,
  title={Attention is not explanation},
  author={Jain, Sarthak and Wallace, Byron C},
  journal={arXiv preprint arXiv:1902.10186},
  year={2019}
}
%
@article{bach2015pixel,
  title={On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation},
  author={Bach, Sebastian and Binder, Alexander and Montavon, Gr{\'e}goire and Klauschen, Frederick and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={PloS one},
  volume={10},
  number={7},
  pages={e0130140},
  year={2015},
  publisher={Public Library of Science San Francisco, CA USA}
}
%
@article{baehrens2010explain,
  title={How to explain individual classification decisions},
  author={Baehrens, David and Schroeter, Timon and Harmeling, Stefan and Kawanabe, Motoaki and Hansen, Katja and M{\"u}ller, Klaus-Robert},
  journal={The Journal of Machine Learning Research},
  volume={11},
  pages={1803--1831},
  year={2010},
  publisher={JMLR. org}
}
%
@inproceedings{shrikumar2017learning,
  title={Learning important features through propagating activation differences},
  author={Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
  booktitle={International conference on machine learning},
  pages={3145--3153},
  year={2017},
  organization={PMlR}
}
%

@article{montavon2019layer,
  title={Layer-wise relevance propagation: an overview},
  author={Montavon, Gr{\'e}goire and Binder, Alexander and Lapuschkin, Sebastian and Samek, Wojciech and M{\"u}ller, Klaus-Robert},
  journal={Explainable AI: interpreting, explaining and visualizing deep learning},
  pages={193--209},
  year={2019},
  publisher={Springer}
}

%
%% MIL models transfer
@inproceedings{shaomultiple,
  title={Do Multiple Instance Learning Models Transfer?},
  author={Shao, Daniel and Chen, Richard J and Song, Andrew H and Runevic, Joel and Lu, Ming Y and Ding, Tong and Mahmood, Faisal},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025}
}
%

@article{alber2019innvestigate,
  title={iNNvestigate neural networks!},
  author={Alber, Maximilian and Lapuschkin, Sebastian and Seegerer, Philipp and H{\"a}gele, Miriam and Sch{\"u}tt, Kristof T and Montavon, Gr{\'e}goire and Samek, Wojciech and M{\"u}ller, Klaus-Robert and D{\"a}hne, Sven and Kindermans, Pieter-Jan},
  journal={Journal of machine learning research},
  volume={20},
  number={93},
  pages={1--8},
  year={2019}
}

%

@inproceedings{gated_att_dauphin2017language,
  title={Language modeling with gated convolutional networks},
  author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David},
  booktitle={International conference on machine learning},
  pages={933--941},
  year={2017},
  organization={PMLR}
}

%

@inproceedings{chen2024camil,
  title={Camil: Causal multiple instance learning for whole slide image classification},
  author={Chen, Kaitao and Sun, Shiliang and Zhao, Jing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  pages={1120--1128},
  year={2024}
}
%

@article{baldi2014dropout,
  title={The dropout learning algorithm},
  author={Baldi, Pierre and Sadowski, Peter},
  journal={Artificial intelligence},
  volume={210},
  pages={78--122},
  year={2014},
  publisher={Elsevier}
}