%%%%%% CNNs and surveys
@article{li2021survey,
  title={A survey of convolutional neural networks: analysis, applications, and prospects},
  author={Li, Zewen and Liu, Fan and Yang, Wenjie and Peng, Shouheng and Zhou, Jun},
  journal={IEEE transactions on neural networks and learning systems},
  volume={33},
  number={12},
  pages={6999--7019},
  year={2021},
  publisher={IEEE}
}

@inproceedings{oquab2015object,
  title={Is object localization for free?-weakly-supervised learning with convolutional neural networks},
  author={Oquab, Maxime and Bottou, L{\'e}on and Laptev, Ivan and Sivic, Josef},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={685--694},
  year={2015}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% Class Activation Map
% CAM
@inproceedings{zhou2016learning,
  title={Learning deep features for discriminative localization},
  author={Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2921--2929},
  year={2016}
} 
% Score-CAM 
@inproceedings{wang2020score, 
  title={Score-CAM: Score-weighted visual explanations for convolutional neural networks},
  author={Wang, Haofan and Wang, Zifan and Du, Mengnan and Yang, Fan and Zhang, Zijian and Ding, Sirui and Mardziel, Piotr and Hu, Xia},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},
  pages={24--25},
  year={2020}
} 

%%%%%% Gradient based
% GradCAM
@inproceedings{selvaraju2017grad,
  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}
}
% LayerCAM
@article{jiang2021layercam,
  title={LayerCAM: Exploring Hierarchical Class Activation Maps For Localization},
  author={Jiang, Peng-Tao and Zhang, Chang-Bin and Hou, Qibin and Cheng, Ming-Ming and Wei, Yunchao},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}

%%%%%% backpropagation
% Integrated Grad
@inproceedings{sundararajan2017axiomatic, 
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle={International conference on machine learning},
  pages={3319--3328},
  year={2017},
  organization={PMLR}
}
%  Guided Backpropagation 
@inproceedings{springenberg2014striving, 
  title={Striving for simplicity: The all convolutional net},
  author={Springenberg, Jost Tobias and Dosovitskiy, Alexey and Brox, Thomas and Riedmiller, Martin},
  booktitle={3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings},
  year={2014}
} 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% XAI eval
@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}
}
@article{arun2021assessing,
  title={Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging},
  author={Arun, Nishanth and Gaw, Nathan and Singh, Praveer and Chang, Ken and Aggarwal, Mehak and Chen, Bryan and Hoebel, Katharina and Gupta, Sharut and Patel, Jay and Gidwani, Mishka and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e200267},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{barnett2021case,
  title={A case-based interpretable deep learning model for classification of mass lesions in digital mammography},
  author={Barnett, Alina Jade and Schwartz, Fides Regina and Tao, Chaofan and Chen, Chaofan and Ren, Yinhao and Lo, Joseph Y and Rudin, Cynthia},
  journal={Nature Machine Intelligence},
  volume={3},
  number={12},
  pages={1061--1070},
  year={2021},
  publisher={Nature Publishing Group UK London}
}
@article{yeh2019fidelity,
  title={On the (in) fidelity and sensitivity of explanations},
  author={Yeh, Chih-Kuan and Hsieh, Cheng-Yu and Suggala, Arun and Inouye, David I and Ravikumar, Pradeep K},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{hase2021out,
  title={The out-of-distribution problem in explainability and search methods for feature importance explanations},
  author={Hase, Peter and Xie, Harry and Bansal, Mohit},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={3650--3666},
  year={2021}
}

@article{qiu2021resisting,
  title={Resisting out-of-distribution data problem in perturbation of xai},
  author={Qiu, Luyu and Yang, Yi and Cao, Caleb Chen and Liu, Jing and Zheng, Yueyuan and Ngai, Hilary Hei Ting and Hsiao, Janet and Chen, Lei},
  journal={arXiv preprint arXiv:2107.14000},
  year={2021}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% self-explanable AI
@article{rudin2019stop,
  title={Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
  author={Rudin, Cynthia},
  journal={Nature machine intelligence},
  volume={1},
  number={5},
  pages={206--215},
  year={2019}
}
@article{brendel2018bagnets,
  title={Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet},
  author={Brendel, Wieland and Bethge, Matthias},
  journal={International Conference on Learning Representations},
  year={2019},
}
@inproceedings{djoumessi2024actually,
  title={This actually looks like that: Proto-BagNets for local and global interpretability-by-design},
  author={Djoumessi, Kerol and Bah, Bubacarr and K{\"u}hlewein, Laura and Berens, Philipp and Koch, Lisa},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={718--728},
  year={2024}
}
@inproceedings{koh2020concept,
  title={Concept bottleneck models},
  author={Koh, Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and Kim, Been and Liang, Percy},
  booktitle={International conference on machine learning},
  pages={5338--5348},
  year={2020},
  organization={PMLR}
}
@article{chen2019looks,
  title={This looks like that: deep learning for interpretable image recognition},
  author={Chen, Chaofan and Li, Oscar and Tao, Daniel and Barnett, Alina and Rudin, Cynthia and Su, Jonathan K},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{bohle2022b,
  title={B-cos networks: Alignment is all we need for interpretability},
  author={B{\"o}hle, Moritz and Fritz, Mario and Schiele, Bernt},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10329--10338},
  year={2022}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% XAI surveys
@inproceedings{he2022survey,
  title={A survey of class activation mapping for the interpretability of convolution neural networks},
  author={He, Mingwei and Li, Bohan and Sun, Songlin},
  booktitle={International Conference On Signal And Information Processing, Networking And Computers},
  pages={399--407},
  year={2022},
  organization={Springer}
}
@article{hossain2023explainable,
  title={Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions},
  author={Hossain, Md Imran and Zamzmi, Ghada and Mouton, Peter R and Salekin, Md Sirajus and Sun, Yu and Goldgof, Dmitry},
  journal={ACM Computing Surveys},
  year={2023},
  publisher={ACM New York, NY}
}
@article{ivanovs2021perturbation,
  title={Perturbation-based methods for explaining deep neural networks: A survey},
  author={Ivanovs, Maksims and Kadikis, Roberts and Ozols, Kaspars},
  journal={Pattern Recognition Letters},
  volume={150},
  pages={228--234},
  month = {08},
  year={2021},
  publisher={Elsevier}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% CNNs architectures
% VGG
@inproceedings{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  booktitle={3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings},
  year={2015}
}

% ResNet50
@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}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% datasets
@article{djoumessi2024inherently,
  title={An Inherently Interpretable AI Model Improves Screening Speed and Accuracy for Early Diabetic Retinopathy},
  author={Djoumessi, Kerol and Huang, Ziwei and Kuehlewein, Laura and Rickmann, Annekatrin and Simon, Natalia and Koch, Lisa M and Berens, Philipp},
  journal={medRxiv},
  pages={2024--06},
  year={2024},
  publisher={Cold Spring Harbor Laboratory Press}
}
@misc{kaggle_dr_detection,
    author = {Dugas, Emma and Jared, Jorge and Cukierski, Will},
    title = {Diabetic Retinopathy Detection},
    publisher = {Kaggle},
    year = {2015},
    url = {https://kaggle.com/competitions/diabetic-retinopathy-detection}
}
@article{rsna_dataset,
  title={Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia},
  author={Shih, George and Wu, Carol C and Halabi, Safwan S and Kohli, Marc D and Prevedello, Luciano M and Cook, Tessa S and Sharma, Arjun and Amorosa, Judith K and Arteaga, Veronica and Galperin-Aizenberg, Maya and others},
  journal={Radiology: Artificial Intelligence},
  volume={1},
  number={1},
  pages={e180041},
  year={2019},
  publisher={Radiological Society of North America}
}
@article{kermany2018identifying,
  title={Identifying medical diagnoses and treatable diseases by image-based deep learning},
  author={Kermany, Daniel S and Goldbaum, Michael and Cai, Wenjia and Valentim, Carolina CS and Liang, Huiying and Baxter, Sally L and McKeown, Alex and Yang, Ge and Wu, Xiaokang and Yan, Fangbing and others},
  journal={cell},
  volume={172},
  number={5},
  pages={1122--1131},
  year={2018},
  publisher={Elsevier}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% others
@inproceedings{aubreville2019transferability,
  title={Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract},
  author={Aubreville, Marc and Goncalves, Miguel and Knipfer, Christian and Oetter, Nicolai and W{\"u}rfl, Tobias and Neumann, Helmut and Stelzle, Florian and Bohr, Christopher and Maier, Andreas},
  booktitle={Biomedical Engineering Systems and Technologies: 11th International Joint Conference, BIOSTEC 2018, Funchal, Madeira, Portugal, January 19--21, 2018, Revised Selected Papers 11},
  pages={67--85},
  year={2019},
  organization={Springer}
}
@inproceedings{donteu2023sparse,
  title={Sparse Activations for Interpretable Disease Grading},
  author={Donteu, Kerol R Djoumessi and Ilanchezian, Indu and K{\"u}hlewein, Laura and Faber, Hanna and Baumgartner, Christian F and Bah, Bubacarr and Berens, Philipp and Koch, Lisa M},
  booktitle={Medical Imaging with Deep Learning},
  year={2023}
}
@article{ayhan2022clinical,
  title={Clinical validation of saliency maps for understanding deep neural networks in ophthalmology},
  author={Ayhan, Murat Se{\c{c}}kin and K{\"u}mmerle, Louis Benedikt and K{\"u}hlewein, Laura and Inhoffen, Werner and Aliyeva, Gulnar and Ziemssen, Focke and Berens, Philipp},
  journal={Medical Image Analysis},
  volume={77},
  pages={102364},
  year={2022},
  publisher={Elsevier}
}
@article{zou2005regularization,
  title={Regularization and variable selection via the elastic net},
  author={Zou, Hui and Hastie, Trevor},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={67},
  number={2},
  pages={301--320},
  year={2005},
  publisher={Oxford University Press}
}
@article{saporta2022benchmarking,
  title={Benchmarking saliency methods for chest X-ray interpretation},
  author={Saporta, Adriel and Gui, Xiaotong and Agrawal, Ashwin and Pareek, Anuj and Truong, Steven QH and Nguyen, Chanh DT and Ngo, Van-Doan and Seekins, Jayne and Blankenberg, Francis G and Ng, Andrew Y and others},
  journal={Nature Machine Intelligence},
  volume={4},
  number={10},
  pages={867--878},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{gunther2025informative,
  title={Informative Post-Hoc Explanations Only Exist for Simple Functions},
  author={G{\"u}nther, Eric and Szabados, Bal{\'a}zs and Bhattacharjee, Robi and Bordt, Sebastian and von Luxburg, Ulrike},
  journal={arXiv preprint arXiv:2508.11441},
  year={2025}
}
@inproceedings{dosovitskiy2020image,
  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 others},
  booktitle    = {9th International Conference on Learning Representations, {ICLR} 2021, Virtual Event, Austria, May 3-7, 2021},
  year={2021}
}

@article{djoumessi2025hybrid,
  title={A hybrid fully convolutional CNN-transformer model for inherently interpretable medical image classification},
  author={Djoumessi, Kerol and Ofosu Mensah, Samuel and Berens, Philipp},
  journal={arXiv e-prints},
  pages={arXiv--2504},
  year={2025}
}

%% appendix
@misc{Mueller_fundus_circle_cropping,
	author = {Mueller, Sarah and Heidrich, Holger and Koch, Lisa M. and Berens, Philipp},
	title = {Fundus circle cropping},
	year = {2023},
	doi = {10.5281/zenodo.10137935},
	url = {https://github.com/berenslab/fundus\_circle\_cropping},
	note = {Version 0.2.0}
}
@article{kokhlikyan2020captum,
  title={Captum: A unified and generic model interpretability library for pytorch},
  author={Kokhlikyan, Narine and Miglani, Vivek and Martin, Miguel and Wang, Edward and Alsallakh, Bilal and Reynolds, Jonathan and Melnikov, Alexander and Kliushkina, Natalia and Araya, Carlos and Yan, Siqi and others},
  journal={arXiv preprint arXiv:2009.07896},
  year={2020}
}
@misc{torcham2020,
    title={TorchCAM: class activation explorer},
    author={François-Guillaume Fernandez},
    year={2020},
    month={March},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/frgfm/torch-cam}}
}
@article{zhang2018top,
  title={Top-down neural attention by excitation backprop},
  author={Zhang, Jianming and Bargal, Sarah Adel and Lin, Zhe and Brandt, Jonathan and Shen, Xiaohui and Sclaroff, Stan},
  journal={International Journal of Computer Vision},
  volume={126},
  number={10},
  pages={1084--1102},
  year={2018},
  publisher={Springer}
}

%% not used

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{zhang2024saliency,
  title={Saliency Maps Give a False Sense of Explainability to Image Classifiers: An Empirical Evaluation across Methods and Metrics},
  author={Zhang, Hanwei and Figueroa, Felipe Torres and Hermanns, Holger},
  booktitle={The 16th Asian Conference on Machine Learning (Conference Track)},
  year={2024}
}

%%%%%% pertubation
@article{petsiuk2018rise,
  title={Rise: Randomized input sampling for explanation of black-box models},
  author={Petsiuk, Vitali and Das, Abir and Saenko, Kate},
  journal={arXiv preprint arXiv:1806.07421},
  year={2018}
}

%%% regulation
@inproceedings{panigutti2023role,
  title={The role of explainable AI in the context of the AI Act},
  author={Panigutti, Cecilia and Hamon, Ronan and Hupont, Isabelle and Fernandez Llorca, David and Fano Yela, Delia and Junklewitz, Henrik and Scalzo, Salvatore and Mazzini, Gabriele and Sanchez, Ignacio and Soler Garrido, Josep and others},
  booktitle={Proceedings of the 2023 ACM conference on fairness, accountability, and transparency},
  pages={1139--1150},
  year={2023}
}

%%% XAI
@article{guidotti2024counterfactual,
  title={Counterfactual explanations and how to find them: literature review and benchmarking},
  author={Guidotti, Riccardo},
  journal={Data Mining and Knowledge Discovery},
  volume={38},
  number={5},
  pages={2770--2824},
  year={2024},
  publisher={Springer}
}

% Opti-CAM   
@article{zhang2024opti, 
  title={Opti-CAM: Optimizing saliency maps for interpretability},
  author={Zhang, Hanwei and Torres, Felipe and Sicre, Ronan and Avrithis, Yannis and Ayache, Stephane},
  journal={Computer Vision and Image Understanding},
  volume={248},
  pages={104101},
  year={2024},
  publisher={Elsevier}
}

% other
@article{mattila2014pneumonia,
  title={Pneumonia. Treatment and diagnosis},
  author={Mattila, Joshua T and Fine, Michael J and Limper, Andrew H and Murray, Patrick R and Chen, Bill B and Lin, Philana Ling},
  journal={Annals of the American Thoracic Society},
  volume={11},
  number={Supplement 4},
  pages={S189--S192},
  year={2014},
  publisher={American Thoracic Society}
}