Adversarial attacks on medical machine learning

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

End-To-End Arguments In System Design

And, S. R., Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984) · Cited by 2,130

Wild patterns: Ten years after the rise of adversarial machine learning

Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563

Robustness May Be at Odds with AccuracyPreprint

Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., & Mądry, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12152 · Cited by 1,513

The Future of the Internet--And How to Stop It

Zittrain, J. L. (2008) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1,371

Untitled work

(2017) · doi:10.48550/arxiv.1712.02779 · Cited by 300

Adversarial Attacks Against Medical Deep Learning SystemsPreprint

Finlayson, S. G., Chung, H. W., Kohane, I. S., & Beam, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.05296 · Cited by 289

Physician manipulation of reimbursement rules for patients: between a rock and a hard place.

Wynia, M. K., Cummins, D. S., VanGeest, J. B., & Wilson, I. B. (2000) · PubMed · doi:10.1001/jama.283.14.1858 · Cited by 238

Corruption: medicine's dirty open secret

Jain, A., Nundy, S., & Abbasi, K. A. (2014) · BMJ · doi:10.1136/bmj.g4184 · Cited by 69

Overbilling vs. Downcoding — The Battle between Physicians and Insurers

Kesselheim, A. S., & Brennan, T. A. (2005) · New England Journal of Medicine · doi:10.1056/nejmp058011 · Cited by 26

Commentary: Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Abramoff, M. D., Mortensen, Z., & Tava, C. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.765936 · Cited by 6

Machine Learning Models for Detecting Software Vulnerabilities

Shah, I. A., Jhanjhi, N. Z., & Brohi, S. N. (2024) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-3703-5.ch001 · Cited by 7

Detection of Deepfake Medical Images Based on Spatial and Frequency Domain Analysis

Pradeepan, P., & Gladston Raj, S. (2024) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn63059.2024.10847427 · Cited by 6

AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance

Ardıç, N., & Dinç, R. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1785381 · Cited by 7

Adversarial Machine Learning: A 20-Year Survey of Attacks, Defenses, and Standards

Tekeste, B., Al-Hussaeni, K., Fung, B. C. M., Alawadhi, I. M., & Fachkha, C. (2026) · IEEE Access · doi:10.1109/access.2026.3690620 · Cited by 7

Definition of the molecular bases of cold and hot properties of traditional Chinese medicine through machine learning

Huang, Z., Li, Y., Cheng, H., Li, G., & Liang, Z. (2022) · Pharmacological Research - Modern Chinese Medicine · doi:10.1016/j.prmcm.2022.100124 · Cited by 6

SUPER-Net: Trustworthy image segmentation via uncertainty propagation in encoder-decoder networks

Carannante, G., Bouaynaya, N. C., Dera, D., Fathallah‐Shaykh, H. M., & Rasool, G. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112503 · Cited by 6

Topological safeguard for evasion attack interpreting the neural networks’ behavior

Echeberria-Barrio, X., Gil, A., Mendialdua, I., & Orduna-Urrutia, R. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110130 · Cited by 6

Using Blockchain and Smart Contracts to Combat Greenwashing in Environmental Disclosures

Gu, Y., Jiang, L., & Dai, J. (2026) · Accounting Horizons · doi:10.2308/horizons-2023-099 · Cited by 7

Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural Networks Against Adversarial Attacks

Meng, Y., Su, J., O’Kane, J. M., & Jamshidi, P. (2020) · Cited by 6

Challenges and opportunities for artificial intelligence in surgery

Andreatta, P., Smith, C. S., Graybill, J. C., Bowyer, M. W., & Elster, E. A. (2021) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/15485129211022855 · Cited by 6

Artificial Intelligence in Healthcare

Aggarwal, N., Matheny, M. E., Shachar, C., Wang, S. X. Y., & Israni, S. T. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.50 · Cited by 6

Machine Learning in Medical Imaging – Clinical Applications and Challenges in Computer Vision

Mehta, O., Liao, Z., Jenkinson, M., Carneiro, G., & Verjans, J. W. H. (2022) · Springer eBooks · doi:10.1007/978-981-19-1223-8_4 · Cited by 6

Comparison of “Human” and Artificial Intelligence Hand-and-Wrist Skeletal Age Estimation in an Epiphysiodesis Cohort

Kluck, D. G., Makarov, M. R., Kanaan, Y., Jo, C.-H., & Birch, J. G. (2022) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.22.00833 · Cited by 6

Adversarial Machine Learning Protection Using the Example of Evasion Attacks on Medical Images

Rudnitskaya, E. A., & Poltavtseva, M. A. (2022) · Automatic Control and Computer Sciences · doi:10.3103/s0146411622080211 · Cited by 6

Graph-based methods coupled with specific distributional distances for adversarial attack detection

Nwaigwe, D., Carboni, L., Mermillod, M., Achard, S., & Dojat, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.007 · Cited by 6

The Security of Deep Learning Defenses in Medical Imaging

Levy, M., Amit, G., Elovici, Y., & Mirsky, Y. (2023) · doi:10.1145/3689942.3694746 · Cited by 6

Bringing robustness against adversarial attacks

Pereira, G. T., & de Carvalho, A. C. P. L. F. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0116-2 · Cited by 6

Towards International Standards for the Evaluation of Artificial Intelligence for Health

Wenzel, M. A., & Wiegand, T. (2019) · ITU Kaleidoscope Academic Conference (ITU K) · doi:10.23919/ituk48006.2019.8996131 · Cited by 7

Towards Human–Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework

Zhang, X., Zheng, X., Liu, B., Wang, X., Mao, W., Zeng, D. D., & Wang, F. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3199819 · Cited by 6

Accurate and adversarially robust classification of medical images and ECG time-series with gradient-free trained sign activation neural networks

Yang, Z., Yang, Y., Xue, Y., Shih, F. Y., Ady, J., & Roshan, U. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313442 · Cited by 5

Improved Adversarial Robustness via Logit Regularization MethodsPreprint

Summers, C., & Dinneen, M. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03749 · Cited by 6

Improved Detection of Adversarial Images Using Deep Neural NetworksPreprint

Gao, Y., & Pan, Y. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05573 · Cited by 6

Local Competition and Stochasticity for Adversarial Robustness in Deep LearningPreprint

Panousis, K. P., Chatzis, S., Alexos, A., & Theodoridis, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.01121 · Cited by 6

Connectome-based machine learning models are vulnerable to subtle data manipulationsPreprint

Rosenblatt, M., Rodriguez, R. X., Westwater, M. L., Horien, C. L., Greene, A. S., Constable, R. T., Noble, S., & Scheinost, D. (2021) · doi:10.31219/osf.io/ptuwe · Cited by 6

Inject Backdoor in Measured Data to Jeopardize Full-Stack Medical Image Analysis System

Yang, Z., Chen, Y., Sun, M., & Zhang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_38 · Cited by 5

Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector

Rey-Puech, P. D., Balabanova, D., & McKee, M. (2025) · The International Journal of Health Planning and Management · doi:10.1002/hpm.70002 · Cited by 5

Towards a Comprehensive Taxonomy of Pointwise Reliability Assessment Techniques for Machine Learning Models

Loureiro, J., Petrella, L., Gil, P. S., Rocha, T., Paredes, S., & Henriques, J. M. O. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3577486 · Cited by 6

A comprehensive review of deepfakes in medical imaging: Ethical concerns, detection techniques and future directions

Pradepan, P., Gladston Raj, S., & George, J. (2025) · Applied Computer Science · doi:10.35784/acs_7054 · Cited by 5

LayerMix: Enhanced Data Augmentation for Robust Deep Learning

Ahmad, H. M., Morle, D., & Rahimi, A. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112332 · Cited by 6

Integrating AI into healthcare systems: A multivocal literature review

Mallardi, G., Calefato, F., Quaranta, L., & Lanubile, F. (2025) · Smart Health · doi:10.1016/j.smhl.2025.100631 · Cited by 6

Building Trustworthy AI in Healthcare

Amadi, C., & Ojo, A. (2025) · IEEE Access · doi:10.1109/access.2025.3648410 · Cited by 9

Trust in Artificial Intelligence: Clinicians Are Essential

Bhatt, U., & Shams, Z. (2021) · Health informatics · doi:10.1007/978-3-030-81030-6_10 · Cited by 5

Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections

Barui, S., Sanyal, P., Rajmohan, K. S., Malik, A., & Dudani, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-20012-1 · Cited by 6

A Comprehensive Taxonomy of Adversarial Attacks on Machine Learning in IoT Application

Praveena, M., Madhumitha, S., Menakadevi, J., & Akkash, V. L. (2023) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca58529.2023.10394928 · Cited by 5

Towards Scalable and Robust Model Versioning

Ding, W., Bhagoji, A. N., Zhao, B. Y., & Zheng, H.-T. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00036 · Cited by 5

Developing robust machine learning models to defend against adversarial attacks in the field of cybersecurity

Khaleel, T. A. (2024) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora61326.2024.10550799 · Cited by 5

Evaluating Sustainability and Social Costs of Adversarial Training in Machine Learning

Hasan, S. M., Shahid, A. R., & Imteaj, A. (2024) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2024.3458350 · Cited by 5

Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense

Hong, I., & Lee, S. (2024) · Applied Sciences · doi:10.3390/app142310872 · Cited by 5

Value Creation for Healthcare Ecosystems through Artificial Intelligence Applied to Physician-to-Physician Communication: A Systematic Review

Rubinstein, B., & Matos, S. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11725-1 · Cited by 6

Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

Haque, S. B. U., Zafar, A., Haq, S. R. U., Haque, S. M. U., Ahmad, M., & Roshan, K. (2025) · Soft Computing · doi:10.1007/s00500-025-10516-z · Cited by 5

GENEOnet: statistical analysis supporting explainability and trustworthiness

Bocchi, G., Frosini, P., Micheletti, A., Pedretti, A., Gratteri, C., Lunghini, F., Beccari, A. R., & Talarico, C. (2025) · Statistics · doi:10.1080/02331888.2025.2478203 · Cited by 5

Interpretable AI Framework for Secure and Reliable Medical Image Analysis in IoMT Systems

Matthew, U. O., Rosa, R. L., Saadi, M., & Rodríguez, D. Z. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3591737 · Cited by 5

A Counterfactual Account of Algorithmic Robustness

Bjerring, J. C., Busch, J., & Munch, L. A. (2025) · Minds and Machines · doi:10.1007/s11023-025-09734-z · Cited by 7

Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges

Grzybowski, A. (2021) · Artificial Intelligence in Ophthalmology · doi:10.1007/978-3-030-78601-4_1 · Cited by 3

A deep-learning toolkit for visualization and interpretation of segmented medical images

Ghosal, S., & Shah, P. P. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100107 · Cited by 5

Improving robustness of a deep learning-based lung-nodule classification model of CT images with respect to image noise

Gao, Y., Xiong, J., Shen, C., & Jia, X. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac3d16 · Cited by 5

Improving Adversarial Robustness of CNNs via Maximum Margin

Wu, J., Xia, Z., & Feng, X. (2022) · Applied Sciences · doi:10.3390/app12157927 · Cited by 5

SCOPE: predicting future diagnoses in office visits using electronic health records

Mukherjee, P., Humbert‐Droz, M., Chen, J. H., & Gevaert, O. (2023) · Scientific Reports · doi:10.1038/s41598-023-38257-9 · Cited by 6

SecureVolt: Enhancing Deep Neural Networks Security via Undervolting

Islam, M. S., Alouani, I., & Khasawneh, K. N. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3296379 · Cited by 5

Towards Inductive Robustness: Distilling and Fostering Wave-Induced Resonance in Transductive GCNs against Graph Adversarial Attacks

Liu, A., Li, W., Li, T., Li, B., Huang, H., & Zhou, P. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29292 · Cited by 5

Privacy preserving technology in ophthalmology

Yang, Y., Chen, X., & Lin, H. (2024) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000001087 · Cited by 6

Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks

Oda, J., & Takemoto, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-03546-y · Cited by 6

Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays

Rafferty, A., Ramaesh, R., & Rajan, A. (2025) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi64645.2025.00071 · Cited by 9

Mobile Apps for Helping Patient-Users: Is It Still Far-Fetched?

Siristatidis, C. S., Pouliakis, A., Karageorgiou, V., & Vogiatzi, P. (2019) · Sustainability · doi:10.3390/su12010106 · Cited by 7

Identification of kernels in a convolutional neural network: connections between the level set equation and deep learning for image segmentation

Actor, J., Fuentes, D., & Rivière, B. M. (2020) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2548871 · Cited by 5

Symmetric Cryptography With Shift 2n-1, Hash Transformation, Optimization-Based Controller for Medical Image Infosecurity: Case Study in Mammographic Image

Chen, P.-Y., Wu, J., Li, C., Kuo, C., Pai, N., & Lin, C. (2020) · IEEE photonics journal · doi:10.1109/jphot.2020.2987769 · Cited by 5

Deep Learning Architectures for Medical Diagnosis

Köse, U., Deperlioğlu, Ö., Alzubi, J. A. A., & Pătruţ, B. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6325-6_2 · Cited by 5

Improving robustness of deep neural networks via large-difference transformation

Wang, L., Wang, C., Li, Y., & Wang, R. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.112 · Cited by 6

Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes

Holloway, L. C., Bezak, E., & Baldock, C. (2021) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-021-01034-x · Cited by 5

A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks

Xiao, Y., Li, J., & Su, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88052-1_4 · Cited by 5

Optimal Feature Manipulation Attacks Against Linear Regression

Li, F., Lai, L., & Cui, S. R. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3115951 · Cited by 5

Predicting facility-based delivery in Zanzibar: The vulnerability of machine learning algorithms to adversarial attacks

Tsai, Y.-T., Fulcher, I., Li, T., Sukums, F. K., & Hedt‐Gauthier, B. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16244 · Cited by 5

Assessing Vulnerabilities of Deep Learning Explainability in Medical Image Analysis Under Adversarial Settings

de Aguiar, E. J., Costa, M. V. L., Traina, C., & Traina, A. J. M. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00184 · Cited by 5

Stress Test Evaluation of Biomedical Word Embeddings

Araujo, V., Carvallo, A., Aspillaga, C., Thorne, C., & Parra, D. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.13 · Cited by 5

Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty

Tuna, Ö. F., Çatak, F. Ö., & Eskil, M. T. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00701-0 · Cited by 5

Artificial Intelligence in Medicine: Diabetes as a Model

Sridhar, G. R., & Lakshmi, G. (2021) · Studies in big data · doi:10.1007/978-981-16-0415-7_14 · Cited by 6

From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)Preprint

Bouraoui, Z., Cornuéjols, A., Denœux, T., Destercke, S., Didier Dubois, A., Guillaume, R., Marques-Silva, J. P., Mengin, J., Prade, H. M., Schockaert, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.06612 · Cited by 8

Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A SurveyPreprint

Zhao, D. Y., Xu, G., Xu, Z., Lukasiewicz, T., Xue, M., & Fu, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00940 · Cited by 5

Local Reweighting for Adversarial TrainingPreprint

Gao, R., Liu, T., Zhou, K., Niu, G., Han, B., & Cheng, J. S.-C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15776 · Cited by 5

Adversarial Attacks Detection Method for Tabular Data

Wawrowski, Ł., Biczyk, P., Ślȩzak, D., & Sikora, M. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7040112 · Cited by 4

Fortifying vision models: A comprehensive survey of defences against adversarial examples

Kumar, S., Srivastava, S., & Banerjea, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113874 · Cited by 4

Adversarial examples detection based on quantum fuzzy convolution neural network

Huang, C., & Zhang, S. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04310-3 · Cited by 4

Towards sustainable adversarial training with successive perturbation generation

Wei, L., & Liao, L. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300474 · Cited by 5

An empirical study of using radiology reports and images to improve intensive care unit mortality prediction

Lin, M., Wang, S., Ding, Y., Zhao, L., Wang, F., & Peng, Y. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae137 · Cited by 5

From Accuracy to Vulnerability: Quantifying the Impact of Adversarial Perturbations on Healthcare AI Models

Brohi, S. N., & Mastoi, Q.-U.-A. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9050114 · Cited by 4

Detecting the Undetected: Machine Learning in Early Disease Diagnosis

Rathi, K., Sharma, S., & Barnwal, A. P. (2025) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.70104 · Cited by 4

Federated learning using quality-based aggregation method for brain tumour segmentation on multimodality medical images

Badaoui, R. E., Bonmati, E., Argyriou, V., & Villarini, B. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200601 · Cited by 4

Augmented Intelligence in Ophthalmology: The Six Rights

Ting, D. S. W., & Al‐Aswad, L. A. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000410 · Cited by 4

Healthcare under siege: the need to improve cybersecurity in the near future

Mertoiu, G.-B., & Meșniță, G. (2022) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb55594.2022.9991541 · Cited by 4

Combating deep fakes by the power of artificial intelligence and block chain in healthcare applications

Chandrappa, K., & Shankar, K. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99481-1.00012-2 · Cited by 4

Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

Hanif, A., Naseer, M., Khan, S. H., Shah, M. A., & Khan, F. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_43 · Cited by 4

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI

Rivera, S. C., Liu, X., Chan, A., Denniston, A. K., Calvert, M., y CONSORT-AI, G. D. T. S.-A., y CONSORT-AI, G. D. S.-A., & y CONSORT-AI, G. D. C. S.-A. (2024) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2024.12 · Cited by 6

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Lapid, R., Dubin, A., & Sipper, M. (2024) · Mathematics · doi:10.3390/math12223451 · Cited by 6

Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging

Ayubcha, C., Sajed, S., Omara, C., Lebouille-Veldman, A. B., Singh, S. B., Lokesha, Y. U., Liu, A. X., Aziz‐Sultan, M. A., Smith, T. R., & Beam, A. L. (2024) · Journal of Imaging · doi:10.3390/jimaging10120319 · Cited by 4

You cannot handle the weather: Progressive amplified adverse-weather-gradient projection adversarial attack

Liu, Y., Chen, M., Zhu, C., Liang, H., & Chen, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126143 · Cited by 4

Actor critic with experience replay‐based automatic treatment planning for prostate cancer intensity modulated radiotherapy

Abrar, M. M., Sapkota, P., Sprouts, D., Jia, X., & Chi, Y. (2025) · Medical Physics · doi:10.1002/mp.17915 · Cited by 4

Role of artificial intelligence in virtual emergency care: a protocol for a systematic review

Shankar, R., Wang, L., Ho, S. H., Liew, M. F., Gollamudi, S. P. K., & Wong, S. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-103084 · Cited by 5

WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions

Colbrook, M. J. (2022) · SIAM Journal on Imaging Sciences · doi:10.1137/21m1455000 · Cited by 4

A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation

Zhang, Y., Shin, S.-Y., Tan, X., & Xiong, B. (2023) · Applied Sciences · doi:10.3390/app13031298 · Cited by 4

Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection

Xiang, K., Zhang, X., She, J., Liu, J., Wang, H., Deng, S., & Jiang, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25395 · Cited by 5

Decentralized Reinforced Anonymous FLchain: a Secure Federated Learning Architecture for the Medical Industry

Wang, C., Wang, S., Zhao, C., Wang, W., Hu, B., Wang, Y., Wang, L., & Chen, Z. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00060 · Cited by 4

SC-PCA: Shape Constraint Physical Camouflage Attack Against Vehicle Detection

Wang, H., Qin, J., Huang, Y., Wu, G., Zhang, H., & Yang, J. (2023) · Journal of Signal Processing Systems · doi:10.1007/s11265-023-01890-8 · Cited by 4

Knowledge distillation vulnerability of DeiT through CNN adversarial attack

Hong, I.-P., & Choi, C. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09297-z · Cited by 7

StyleAdv: A Usable Privacy Framework Against Facial Recognition with Adversarial Image Editing

Le, M., & Carlsson, N. F. (2024) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2024-0043 · Cited by 4

Detection of sensors used for adversarial examples against machine learning models

Kurniawan, A., Ohsita, Y., Maisuria, S., & Murata, M. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103021 · Cited by 4

STPD: Defending against ℓ0 -norm attacks with space transformation

Chen, J., Cao, J., Liang, Z., Cui, X., Yu, L., & Li, W. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.009 · Cited by 4

Data Poisoning Attack and Defenses in Connectome-Based Predictive Models

Rosenblatt, M., & Scheinost, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23223-7_1 · Cited by 4

Adversarial Machine Learning in e-Health: Attacking a Smart Prescription System

Gaglio, S., Giammanco, A., Re, G. L., & Morana, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08421-8_34 · Cited by 4

Adversarial Examples for ElectrocardiogramsPreprint

Han, X., Hu, Y., Foschini, L., Chinitz, L. A., Jankelson, L., & Ranganath, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05163 · Cited by 4

PhD Forum: Learning Adaptive Adversarial Attacks for Medical AI via Reinforcement Learning

Fabiano, M. (2026) · doi:10.1109/smartcomp-companion70724.2026.00091

Reply to “Malicious Adversarial Attacks on Medical Image Analysis”

Desjardins, B., & Ritenour, E. R. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.24244

Adversarial Threats to AI Systems: A Critical Review of Vulnerabilities in Machine Learning Security

Nataala, A. A. (2026) · doi:10.13140/rg.2.2.12207.50081

Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods

Filipovich, I., & Kovalev, V. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96129-5.00004-4 · Cited by 4

Malicious Adversarial Attacks on Medical Image Analysis

Thomas C. Winter (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23250 · Cited by 4