@article{siegel2024cancer,
  title={Cancer statistics, 2024},
  author={Siegel, Rebecca L and Giaquinto, Angela N and Jemal, Ahmedin},
  journal={CA: a cancer journal for clinicians},
  volume={74},
  number={1},
  pages={12--49},
  year={2024},
  publisher={Wiley Online Library}
}

@article{diepgen2002epidemiology,
  title={The epidemiology of skin cancer},
  author={Diepgen, Thomas L and Mahler, V},
  journal={British Journal of Dermatology},
  volume={146},
  number={s61},
  pages={1--6},
  year={2002},
  publisher={Blackwell Science, Ltd Oxford, UK}
}

@article{nikolaou2014emerging,
  title={Emerging trends in the epidemiology of melanoma},
  author={Nikolaou, V and Stratigos, AJ},
  journal={British journal of dermatology},
  volume={170},
  number={1},
  pages={11--19},
  year={2014},
  publisher={Blackwell Publishing Ltd Oxford, UK}
}

@article{resneck2004dermatology,
  title={The dermatology workforce shortage},
  author={Resneck Jr, Jack and Kimball, Alexa B},
  journal={Journal of the American Academy of Dermatology},
  volume={50},
  number={1},
  pages={50--54},
  year={2004},
  publisher={Elsevier}
}

@article{porter2018predictions,
  title={Predictions, surprises, and the future of the dermatology workforce},
  author={Porter, Martina L and Kimball, Alexa B},
  journal={JAMA dermatology},
  volume={154},
  number={11},
  pages={1253--1255},
  year={2018},
  publisher={American Medical Association}
}

@article{ehrlich2017trends,
  title={Trends in dermatology practices and the implications for the workforce},
  author={Ehrlich, Alison and Kostecki, James and Olkaba, Helen},
  journal={Journal of the American Academy of Dermatology},
  volume={77},
  number={4},
  pages={746--752},
  year={2017},
  publisher={Elsevier}
}

@article{chan2020machine,
  title={Machine learning in dermatology: current applications, opportunities, and limitations},
  author={Chan, Stephanie and Reddy, Vidhatha and Myers, Bridget and Thibodeaux, Quinn and Brownstone, Nicholas and Liao, Wilson},
  journal={Dermatology and therapy},
  volume={10},
  pages={365--386},
  year={2020},
  publisher={Springer}
}

@article{kong2021review,
  title={Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time?},
  author={Kong, Fleur W and Horsham, Caitlin and Ngoo, Alexander and Soyer, H Peter and Janda, Monika},
  journal={International Journal of Dermatology},
  volume={60},
  number={3},
  pages={289--308},
  year={2021},
  publisher={Wiley Online Library}
}

@article{freeman2020algorithm,
  title={Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies},
  author={Freeman, Karoline and Dinnes, Jacqueline and Chuchu, Naomi and Takwoingi, Yemisi and Bayliss, Sue E and Matin, Rubeta N and Jain, Abhilash and Walter, Fiona M and Williams, Hywel C and Deeks, Jonathan J},
  journal={bmj},
  volume={368},
  year={2020},
  publisher={British Medical Journal Publishing Group}
}

@article{ngoo2018efficacy,
  title={Efficacy of smartphone applications in high-risk pigmented lesions},
  author={Ngoo, Alexander and Finnane, Anna and McMeniman, Erin and Tan, Jean-Marie and Janda, Monika and Soyer, H Peter},
  journal={Australasian Journal of Dermatology},
  volume={59},
  number={3},
  pages={e175--e182},
  year={2018},
  publisher={Wiley Online Library}
}

@article{rat2018use,
  title={Use of smartphones for early detection of melanoma: systematic review},
  author={Rat, C{\'e}dric and Hild, Sandrine and Rault S{\'e}randour, Julie and Gaultier, Aur{\'e}lie and Quereux, Gaelle and Dreno, Brigitte and Nguyen, Jean-Michel},
  journal={Journal of medical Internet research},
  volume={20},
  number={4},
  pages={e135},
  year={2018},
  publisher={JMIR Publications Toronto, Canada}
}

@article{kassianos2015smartphone,
  title={Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review},
  author={Kassianos, Angelos P and Emery, JD and Murchie, Peter and Walter, Fiona Mary},
  journal={British Journal of Dermatology},
  volume={172},
  number={6},
  pages={1507--1518},
  year={2015},
  publisher={Blackwell Publishing Ltd Oxford, UK}
}

@article{udrea2020accuracy,
  title={Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms},
  author={Udrea, Andreaa and Mitra, GD and Costea, D and Noels, EC and Wakkee, Marlies and Siegel, DM and de Carvalho, TM and Nijsten, TEC},
  journal={Journal of the European Academy of Dermatology and Venereology},
  volume={34},
  number={3},
  pages={648--655},
  year={2020},
  publisher={Wiley Online Library}
}

@article{wolf2013diagnostic,
  title={Diagnostic inaccuracy of smartphone applications for melanoma detection},
  author={Wolf, Joel A and Moreau, Jacqueline F and Akilov, Oleg and Patton, Timothy and English, Joseph C and Ho, Jonhan and Ferris, Laura K},
  journal={JAMA dermatology},
  volume={149},
  number={4},
  pages={422--426},
  year={2013},
  publisher={American Medical Association}
}

@article{deeks2020sensitivity,
  title={Sensitivity and specificity of SkinVision are likely to have been overestimated},
  author={Deeks, JJ and Dinnes, J and Williams, HC},
  journal={J Eur Acad Dermatol Venereol},
  volume={34},
  number={10},
  pages={e582--3},
  year={2020}
}

@article{shen2017deep,
  title={Deep learning in medical image analysis},
  author={Shen, Dinggang and Wu, Guorong and Suk, Heung-Il},
  journal={Annual review of biomedical engineering},
  volume={19},
  number={1},
  pages={221--248},
  year={2017},
  publisher={Annual Reviews}
}

@article{esteva2017dermatologist,
  title={Dermatologist-level classification of skin cancer with deep neural networks},
  author={Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian},
  journal={nature},
  volume={542},
  number={7639},
  pages={115--118},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{sokolov2022dynamics,
  title={Dynamics of the neural network accuracy in the context of modernization of the algorithms of skin pathology recognition},
  author={Sokolov, Kirill and Shpudeiko, Viktor},
  journal={Indian Journal of Dermatology},
  volume={67},
  number={3},
  pages={312},
  year={2022},
  publisher={Medknow}
}

@article{goyal2020artificial,
  title={Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities},
  author={Goyal, Manu and Knackstedt, Thomas and Yan, Shaofeng and Hassanpour, Saeed},
  journal={Computers in biology and medicine},
  volume={127},
  pages={104065},
  year={2020},
  publisher={Elsevier}
}

@article{dascalu2019skin,
  title={Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope},
  author={Dascalu, Avi and David, EO},
  journal={EBioMedicine},
  volume={43},
  pages={107--113},
  year={2019},
  publisher={Elsevier}
}

@article{takiddin2021artificial,
  title={Artificial intelligence for skin cancer detection: scoping review},
  author={Takiddin, Abdulrahman and Schneider, Jens and Yang, Yin and Abd-Alrazaq, Alaa and Househ, Mowafa},
  journal={Journal of medical Internet research},
  volume={23},
  number={11},
  pages={e22934},
  year={2021},
  publisher={JMIR Publications Toronto, Canada}
}

@article{brinker2018skin,
  title={Skin cancer classification using convolutional neural networks: systematic review},
  author={Brinker, Titus Josef and Hekler, Achim and Utikal, Jochen Sven and Grabe, Niels and Schadendorf, Dirk and Klode, Joachim and Berking, Carola and Steeb, Theresa and Enk, Alexander H and Von Kalle, Christof},
  journal={Journal of medical Internet research},
  volume={20},
  number={10},
  pages={e11936},
  year={2018},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@article{goceri2021diagnosis,
  title={Diagnosis of skin diseases in the era of deep learning and mobile technology},
  author={Goceri, Evgin},
  journal={Computers in Biology and Medicine},
  volume={134},
  pages={104458},
  year={2021},
  publisher={Elsevier}
}

@article{kousis2022deep,
  title={Deep learning methods for accurate skin cancer recognition and mobile application},
  author={Kousis, Ioannis and Perikos, Isidoros and Hatzilygeroudis, Ioannis and Virvou, Maria},
  journal={Electronics},
  volume={11},
  number={9},
  pages={1294},
  year={2022},
  publisher={MDPI}
}

@article{kranke2023new,
  title={New AI-algorithms on smartphones to detect skin cancer in a clinical setting—A validation study},
  author={Kr{\"a}nke, Teresa and Tripolt-Droschl, Katharina and R{\"o}d, Lukas and Hofmann-Wellenhof, Rainer and Koppitz, Michael and Tripolt, Michael},
  journal={Plos one},
  volume={18},
  number={2},
  pages={e0280670},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}


@article{smak2023artificial,
  title={An artificial intelligence based app for skin cancer detection evaluated in a population based setting},
  author={Smak Gregoor, Anna M and Sangers, Tobias E and Bakker, Lytske J and Hollestein, Loes and Uyl--de Groot, Carin A and Nijsten, Tamar and Wakkee, Marlies},
  journal={NPJ digital medicine},
  volume={6},
  number={1},
  pages={90},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{liu2019comparison,
  title={A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis},
  author={Liu, Xiaoxuan and Faes, Livia and Kale, Aditya U and Wagner, Siegfried K and Fu, Dun Jack and Bruynseels, Alice and Mahendiran, Thushika and Moraes, Gabriella and Shamdas, Mohith and Kern, Christoph and others},
  journal={The lancet digital health},
  volume={1},
  number={6},
  pages={e271--e297},
  year={2019},
  publisher={Elsevier}
}

@article{yuan2019adversarial,
  title={Adversarial examples: Attacks and defenses for deep learning},
  author={Yuan, Xiaoyong and He, Pan and Zhu, Qile and Li, Xiaolin},
  journal={IEEE transactions on neural networks and learning systems},
  volume={30},
  number={9},
  pages={2805--2824},
  year={2019},
  publisher={IEEE}
}

@article{chakraborty2021survey,
  title={A survey on adversarial attacks and defences},
  author={Chakraborty, Anirban and Alam, Manaar and Dey, Vishal and Chattopadhyay, Anupam and Mukhopadhyay, Debdeep},
  journal={CAAI Transactions on Intelligence Technology},
  volume={6},
  number={1},
  pages={25--45},
  year={2021},
  publisher={Wiley Online Library}
}

@article{akhtar2021advances,
  title={Advances in adversarial attacks and defenses in computer vision: A survey},
  author={Akhtar, Naveed and Mian, Ajmal and Kardan, Navid and Shah, Mubarak},
  journal={IEEE Access},
  volume={9},
  pages={155161--155196},
  year={2021},
  publisher={IEEE}
}

@article{finlayson2019adversarial,
  title={Adversarial attacks on medical machine learning},
  author={Finlayson, Samuel G and Bowers, John D and Ito, Joichi and Zittrain, Jonathan L and Beam, Andrew L and Kohane, Isaac S},
  journal={Science},
  volume={363},
  number={6433},
  pages={1287--1289},
  year={2019},
  publisher={American Association for the Advancement of Science}
}

@inproceedings{asgari2018vulnerability,
  title={Vulnerability analysis of chest x-ray image classification against adversarial attacks},
  author={Asgari Taghanaki, Saeid and Das, Arkadeep and Hamarneh, Ghassan},
  booktitle={Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings 1},
  pages={87--94},
  year={2018},
  organization={Springer}
}

@article{cheng2020adversarial,
  title={Adversarial perturbation on MRI modalities in brain tumor segmentation},
  author={Cheng, Guohua and Ji, Hongli},
  journal={IEEE Access},
  volume={8},
  pages={206009--206015},
  year={2020},
  publisher={IEEE}
}

@article{larrazabal2020gender,
  title={Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis},
  author={Larrazabal, Agostina J and Nieto, Nicol{\'a}s and Peterson, Victoria and Milone, Diego H and Ferrante, Enzo},
  journal={Proceedings of the National Academy of Sciences},
  volume={117},
  number={23},
  pages={12592--12594},
  year={2020},
  publisher={National Acad Sciences}
}

@article{vokinger2021mitigating,
  title={Mitigating bias in machine learning for medicine},
  author={Vokinger, Kerstin N and Feuerriegel, Stefan and Kesselheim, Aaron S},
  journal={Communications medicine},
  volume={1},
  number={1},
  pages={25},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{glocker2023risk,
  title={Risk of bias in chest radiography deep learning foundation models},
  author={Glocker, Ben and Jones, Charles and Roschewitz, M{\'e}lanie and Winzeck, Stefan},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={6},
  pages={e230060},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{hirano2021universal,
  title={Universal adversarial attacks on deep neural networks for medical image classification},
  author={Hirano, Hokuto and Minagi, Akinori and Takemoto, Kazuhiro},
  journal={BMC medical imaging},
  volume={21},
  pages={1--13},
  year={2021},
  publisher={Springer}
}

@article{kaissis2020secure,
  title={Secure, privacy-preserving and federated machine learning in medical imaging},
  author={Kaissis, Georgios A and Makowski, Marcus R and R{\"u}ckert, Daniel and Braren, Rickmer F},
  journal={Nature Machine Intelligence},
  volume={2},
  number={6},
  pages={305--311},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{qayyum2020secure,
  title={Secure and robust machine learning for healthcare: A survey},
  author={Qayyum, Adnan and Qadir, Junaid and Bilal, Muhammad and Al-Fuqaha, Ala},
  journal={IEEE Reviews in Biomedical Engineering},
  volume={14},
  pages={156--180},
  year={2020},
  publisher={IEEE}
}

@article{kaviani2022adversarial,
  title={Adversarial attacks and defenses on AI in medical imaging informatics: A survey},
  author={Kaviani, Sara and Han, Ki Jin and Sohn, Insoo},
  journal={Expert Systems with Applications},
  volume={198},
  pages={116815},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{croce2020reliable,
  title={Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks},
  author={Croce, Francesco and Hein, Matthias},
  booktitle={International conference on machine learning},
  pages={2206--2216},
  year={2020},
  organization={PMLR}
}

@inproceedings{
tramer2018ensemble,
title={Ensemble Adversarial Training: Attacks and Defenses},
author={Florian Tramèr and Alexey Kurakin and Nicolas Papernot and Ian Goodfellow and Dan Boneh and Patrick McDaniel},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rkZvSe-RZ},
}

@article{aliyu2022statistical,
  title={Statistical detection of adversarial examples in blockchain-based federated forest in-vehicle network intrusion detection systems},
  author={Aliyu, Ibrahim and Van Engelenburg, Selinde and Mu’Azu, Muhammed Bashir and Kim, Jinsul and Lim, Chang Gyoon},
  journal={IEEE Access},
  volume={10},
  pages={109366--109384},
  year={2022},
  publisher={IEEE}
}

@inproceedings{zhang2020hrae,
  title={HRAE: hardware-assisted randomization against adversarial example attacks},
  author={Zhang, Jiliang and Peng, Shuang and Hu, Yupeng and Peng, Fei and Hu, Wei and Lai, Jinmei and Ye, Jing and Wang, Xiangqi},
  booktitle={2020 IEEE 29th Asian test symposium (ATS)},
  pages={1--6},
  year={2020},
  organization={IEEE}
}


@InProceedings{pmlr-v115-guo20a,
  title = 	 {Low Frequency Adversarial Perturbation},
  author =       {Guo, Chuan and Frank, Jared S. and Weinberger, Kilian Q.},
  booktitle = 	 {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference},
  pages = 	 {1127--1137},
  year = 	 {2020},
  editor = 	 {Adams, Ryan P. and Gogate, Vibhav},
  volume = 	 {115},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {22--25 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v115/guo20a/guo20a.pdf},
  url = 	 {https://proceedings.mlr.press/v115/guo20a.html},
  abstract = 	 {Adversarial images aim to change a target model’s decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this search problem costly in terms of query complexity. In this paper we propose to restrict the search for adversarial images to a low frequency domain. This approach is readily compatible with many existing black-box attack frameworks and consistently reduces their query cost by 2 to 4 times. Further, we can circumvent image transformation defenses even when both the model and the defense strategy are unknown. Finally, we demonstrate the efficacy of this technique by fooling the Google Cloud Vision platform with an unprecedented low number of model queries.}
}

@inproceedings{
madry2018towards,
title={Towards Deep Learning Models Resistant to Adversarial Attacks},
author={Aleksander Madry and Aleksandar Makelov and Ludwig Schmidt and Dimitris Tsipras and Adrian Vladu},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rJzIBfZAb},
}

@inproceedings{
samangouei2018defensegan,
title={Defense-{GAN}: Protecting Classifiers Against Adversarial Attacks Using Generative Models},
author={Pouya Samangouei and Maya Kabkab and Rama Chellappa},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=BkJ3ibb0-},
}

@article{aljedaani2021challenges,
  title={Challenges with developing secure mobile health applications: Systematic review},
  author={Aljedaani, Bakheet and Babar, M Ali and others},
  journal={JMIR mHealth and uHealth},
  volume={9},
  number={6},
  pages={e15654},
  year={2021},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@article{bulusu2020anomalous,
  title={Anomalous example detection in deep learning: A survey},
  author={Bulusu, Saikiran and Kailkhura, Bhavya and Li, Bo and Varshney, Pramod K and Song, Dawn},
  journal={IEEE Access},
  volume={8},
  pages={132330--132347},
  year={2020},
  publisher={IEEE}
}

@inproceedings{carlini2017adversarial,
  title={Adversarial examples are not easily detected: Bypassing ten detection methods},
  author={Carlini, Nicholas and Wagner, David},
  booktitle={Proceedings of the 10th ACM workshop on artificial intelligence and security},
  pages={3--14},
  year={2017}
}


@inproceedings{
tsipras2018robustness,
title={Robustness May Be at Odds with Accuracy},
author={Dimitris Tsipras and Shibani Santurkar and Logan Engstrom and Alexander Turner and Aleksander Madry},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=SyxAb30cY7},
}

@article{sorin2023adversarial,
  title={Adversarial attacks in radiology--A systematic review},
  author={Sorin, Vera and Soffer, Shelly and Glicksberg, Benjamin S and Barash, Yiftach and Konen, Eli and Klang, Eyal},
  journal={European Journal of Radiology},
  pages={111085},
  year={2023},
  publisher={Elsevier}
}

@incollection{kurakin2018adversarial,
  title={Adversarial examples in the physical world},
  author={Kurakin, Alexey and Goodfellow, Ian J and Bengio, Samy},
  booktitle={Artificial intelligence safety and security},
  pages={99--112},
  year={2018},
  publisher={Chapman and Hall/CRC}
}

@article{abernethy2022promise,
  title={The promise of digital health: then, now, and the future},
  author={Abernethy, Amy and Adams, Laura and Barrett, Meredith and Bechtel, Christine and Brennan, Patricia and Butte, Atul and Faulkner, Judith and Fontaine, Elaine and Friedhoff, Stephen and Halamka, John and others},
  journal={NAM perspectives},
  volume={2022},
  year={2022},
  publisher={National Academy of Medicine}
}

@article{hirano2020vulnerability,
  title={Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks},
  author={Hirano, Hokuto and Koga, Kazuki and Takemoto, Kazuhiro},
  journal={Plos one},
  volume={15},
  number={12},
  pages={e0243963},
  year={2020},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{wei2024physical,
  title={Physical adversarial attack meets computer vision: A decade survey},
  author={Wei, Hui and Tang, Hao and Jia, Xuemei and Wang, Zhixiang and Yu, Hanxun and Li, Zhubo and Satoh, Shin'ichi and Van Gool, Luc and Wang, Zheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  publisher={IEEE}
}

@article{ren2021adversarial,
  title={Adversarial examples: attacks and defenses in the physical world},
  author={Ren, Huali and Huang, Teng and Yan, Hongyang},
  journal={International Journal of Machine Learning and Cybernetics},
  volume={12},
  number={11},
  pages={3325--3336},
  year={2021},
  publisher={Springer}
}

@inproceedings{eykholt2018robust,
  title={Robust physical-world attacks on deep learning visual classification},
  author={Eykholt, Kevin and Evtimov, Ivan and Fernandes, Earlence and Li, Bo and Rahmati, Amir and Xiao, Chaowei and Prakash, Atul and Kohno, Tadayoshi and Song, Dawn},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1625--1634},
  year={2018}
}

@inproceedings{thys2019fooling,
  title={Fooling automated surveillance cameras: adversarial patches to attack person detection},
  author={Thys, Simen and Van Ranst, Wiebe and Goedem{\'e}, Toon},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},
  pages={0--0},
  year={2019}
}

@inproceedings{gnanasambandam2021optical,
  title={Optical adversarial attack},
  author={Gnanasambandam, Abhiram and Sherman, Alex M and Chan, Stanley H},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={92--101},
  year={2021}
}

@inproceedings{wang2023rfla,
  title={Rfla: A stealthy reflected light adversarial attack in the physical world},
  author={Wang, Donghua and Yao, Wen and Jiang, Tingsong and Li, Chao and Chen, Xiaoqian},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4455--4465},
  year={2023}
}

@inproceedings{li2019adversarial,
  title={Adversarial camera stickers: A physical camera-based attack on deep learning systems},
  author={Li, Juncheng and Schmidt, Frank and Kolter, Zico},
  booktitle={International conference on machine learning},
  pages={3896--3904},
  year={2019},
  organization={PMLR}
}

@article{cheng2019improving,
  title={Improving black-box adversarial attacks with a transfer-based prior},
  author={Cheng, Shuyu and Dong, Yinpeng and Pang, Tianyu and Su, Hang and Zhu, Jun},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{poursaeed2018generative,
  title={Generative adversarial perturbations},
  author={Poursaeed, Omid and Katsman, Isay and Gao, Bicheng and Belongie, Serge},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4422--4431},
  year={2018}
}

@inproceedings{moosavi2017universal,
  title={Universal adversarial perturbations},
  author={Moosavi-Dezfooli, Seyed-Mohsen and Fawzi, Alhussein and Fawzi, Omar and Frossard, Pascal},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1765--1773},
  year={2017}
}

@inproceedings{papernot2017practical,
  title={Practical black-box attacks against machine learning},
  author={Papernot, Nicolas and McDaniel, Patrick and Goodfellow, Ian and Jha, Somesh and Celik, Z Berkay and Swami, Ananthram},
  booktitle={Proceedings of the 2017 ACM on Asia conference on computer and communications security},
  pages={506--519},
  year={2017}
}

@inproceedings{demontis2019adversarial,
  title={Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks},
  author={Demontis, Ambra and Melis, Marco and Pintor, Maura and Jagielski, Matthew and Biggio, Battista and Oprea, Alina and Nita-Rotaru, Cristina and Roli, Fabio},
  booktitle={28th USENIX security symposium (USENIX security 19)},
  pages={321--338},
  year={2019}
}

@article{bortsova2021adversarial,
  title={Adversarial attack vulnerability of medical image analysis systems: Unexplored factors},
  author={Bortsova, Gerda and Gonz{\'a}lez-Gonzalo, Cristina and Wetstein, Suzanne C and Dubost, Florian and Katramados, Ioannis and Hogeweg, Laurens and Liefers, Bart and van Ginneken, Bram and Pluim, Josien PW and Veta, Mitko and others},
  journal={Medical Image Analysis},
  volume={73},
  pages={102141},
  year={2021},
  publisher={Elsevier}
}

@article{ma2021understanding,
  title={Understanding adversarial attacks on deep learning based medical image analysis systems},
  author={Ma, Xingjun and Niu, Yuhao and Gu, Lin and Wang, Yisen and Zhao, Yitian and Bailey, James and Lu, Feng},
  journal={Pattern Recognition},
  volume={110},
  pages={107332},
  year={2021},
  publisher={Elsevier}
}

@article{koga2022simple,
  title={Simple black-box universal adversarial attacks on deep neural networks for medical image classification},
  author={Koga, Kazuki and Takemoto, Kazuhiro},
  journal={Algorithms},
  volume={15},
  number={5},
  pages={144},
  year={2022},
  publisher={MDPI}
}

@article{minagi2022natural,
  title={Natural images allow universal adversarial attacks on medical image classification using deep neural networks with transfer learning},
  author={Minagi, Akinori and Hirano, Hokuto and Takemoto, Kauzhiro},
  journal={Journal of Imaging},
  volume={8},
  number={2},
  pages={38},
  year={2022},
  publisher={MDPI}
}

@article{codella2019skin,
  title={Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic)},
  author={Codella, Noel and Rotemberg, Veronica and Tschandl, Philipp and Celebi, M Emre and Dusza, Stephen and Gutman, David and Helba, Brian and Kalloo, Aadi and Liopyris, Konstantinos and Marchetti, Michael and others},
  journal={arXiv preprint arXiv:1902.03368},
  year={2019}
}

@article{tschandl2018ham10000,
  title={The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
  author={Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
  journal={Scientific data},
  volume={5},
  number={1},
  pages={1--9},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

@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{howard2017mobilenets,
  title={Mobilenets: Efficient convolutional neural networks for mobile vision applications},
  author={Howard, Andrew G},
  journal={arXiv preprint arXiv:1704.04861},
  year={2017}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@inproceedings{cubuk2020randaugment,
  title={Randaugment: Practical automated data augmentation with a reduced search space},
  author={Cubuk, Ekin D and Zoph, Barret and Shlens, Jonathon and Le, Quoc V},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},
  pages={702--703},
  year={2020}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@software{ming2022imbalanced,
  author = {Ming},
  title = {Imbalanced Dataset Sampler},
  url = {https://github.com/ufoym/imbalanced-dataset-sampler},
  version = {0.1.2},
  month = {5},
  year = {2022}
}

@software{fotocheckskin,
  author = {{Turion Development}},
  title = {Blemish Types, Skin Cancer ID},
  url = {https://play.google.com/store/apps/details?id=com.FotoCheckSkin},
  version = {2.21},
  month = {9},
  year = {2024}
}

@inproceedings{tan2019efficientnet,
  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
  author={Tan, Mingxing and Le, Quoc},
  booktitle={International conference on machine learning},
  pages={6105--6114},
  year={2019},
  organization={PMLR}
}

@article{richardson2022framework,
  title={A framework for digital health equity},
  author={Richardson, Safiya and Lawrence, Katharine and Schoenthaler, Antoinette M and Mann, Devin},
  journal={NPJ digital medicine},
  volume={5},
  number={1},
  pages={119},
  year={2022},
  publisher={Nature Publishing Group UK London}
}