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

Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology

Irmakci, I., Nateghi, R., Zhou, R., Vescovo, M., Saft, M., Ross, A. E., Yang, X. J., Cooper, L., & Goldstein, J. A. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100422 · Cited by 12

Self-adaptive logit balancing for deep neural network robustness: Defence and detection of adversarial attacks

Wei, J., Yao, L., & Meng, Q. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.013 · Cited by 11

Convergence to minima for the continuous version of Backtracking Gradient DescentPreprint

Truong, T. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.04221 · Cited by 11

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancerPreprint

Diao, J. A., Chui, W. F., Wang, J. K., Mitchell, R. N., Rao, S. K., Resnick, M. B., Lahiri, A., Maheshwari, C., Glass, B., Mountain, V., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.02.233197 · Cited by 12

ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefensePreprint

Meng, Y., Su, J., O'Kane, J., & Jamshidi, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00308 · Cited by 11

Stabilizing Deep Tomographic ReconstructionPreprint

Wu, W., Hu, D., Cong, W., Shan, H., Wang, S., Niu, C., Yan, P., Yu, H., Vardhanabhuti, V., & Wang, G. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3974940 · Cited by 11

Learning to Live with Strange Error: Beyond Trustworthiness in Artificial Intelligence Ethics

Rathkopf, C. A., & Heinrichs, B. (2023) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180122000688 · Cited by 10

A Survey of Threats to Research Literature-dependent Medical AI Solutions

Saini, S., & Saxena, N. (2023) · ACM Computing Surveys · doi:10.1145/3592597 · Cited by 11

Reversing skin cancer adversarial examples by multiscale diffusive and denoising aggregation mechanism

Wang, Y., Li, Y., Shen, Z., & Qiao, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107310 · Cited by 10

Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines

Lee, R. Y., Ziehm, A. I., Ullrich, L. A., & Stawicki, S. P. (2023) · Artificial intelligence · doi:10.5772/intechopen.112691 · Cited by 10

Cyberattacks on Canadian health information systems

Harish, V., Ackery, A., Grant, K. L., Jamieson, T., & Mehta, S. (2023) · Canadian Medical Association Journal · doi:10.1503/cmaj.230436 · Cited by 10

Exploring dermoscopic structures for melanoma lesions' classification

Malik, F. S., Yousaf, M. H., Sial, H. A., & Viriri, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1366312 · Cited by 11

Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?

Hatherley, J. J. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2024-109905 · Cited by 10

Next-generation generalist energy artificial intelligence for navigating smart energy

Zhu, X., Chen, S., Liang, X., Jin, X., & Du, Z. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102192 · Cited by 12

PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning

Hussein, N., Shamshad, F., Naseer, M., & Nandakumar, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_65 · Cited by 10

Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging

Kanca, E., Ayas, S., Kablan, E. B., & Ekіncі, M. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03226-5 · Cited by 10

Adversarially Enhanced Learning (AEL): Robust lightweight deep learning approach for radiology image classification against adversarial attacks

Singh, A., Singh, M. P., & Singh, A. K. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105405 · Cited by 10

Improving Robustness and Reliability in Medical Image Classification With Latent-Guided Diffusion and Nested-Ensembles

Shen, X., Huang, H., Nichyporuk, B., & Arbel, T. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3583974 · Cited by 15

A tailor-made 3-dimensional directional Haar semi-tight framelet for pMRI reconstruction

Li, Y.-R., Shen, L., & Zhuang, X. (2022) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2022.04.003 · Cited by 10

Adversarial attacks on deep learning networks in image classification based on Smell Bees Optimization Algorithm

Ali, Y. M. B. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.10.022 · Cited by 10

Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

Lim, N. T., Kuan, M. Y., Pu, M., Lim, M. K., & Chong, C. Y. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956543 · Cited by 10

Similarity-based Deep Neural Network to Detect Imperceptible Adversarial Attacks

Soares, E. A., Angelov, P. P., & Suri, N. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022016 · Cited by 10

MISPSO-Attack: An efficient adversarial watermarking attack based on multiple initial solution particle swarm optimization

Zuo, X., Wang, X., Zhang, W., & Wang, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110777 · Cited by 10

Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness

Wu, Y., Chow, K.-H., Wei, W., & Liu, L. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00074 · Cited by 10

Minimal Adversarial Perturbations in Mobile Health Applications: The Epileptic Brain Activity Case Study

Aminifar, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053706 · Cited by 10

Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising

Aneja, S., Aneja, N., Abas, P. E., & Naim, A. G. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i3.pp961-968 · Cited by 10

Editorial: Interpretable and explainable machine learning models in oncology

Hrinivich, W. T., Wang, T., & Wang, C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1184428 · Cited by 15

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Çatak, F. Ö., Kuzlu, M., Tang, H., Çatak, E., & Zhao, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3206012 · Cited by 9

Enhancing the Sustainability of Smart Healthcare Applications with XAI

Chen, T. (2023) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-37146-2_5 · Cited by 9

Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing

Farbiz, F., Aggarwal, S., Maszczyk, T. K., Habibullah, M. S., & Hamadicharef, B. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02482-4 · Cited by 9

Enhancing the adversarial robustness in medical image classification: exploring adversarial machine learning with vision transformers-based models

Kanca, E., Ayas, S., Kablan, E. B., & Ekіncі, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10516-4 · Cited by 9

Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review

Mashmool, A., Delzanno, G., Saadatfar, H., Ahmad, A., Koschke, R., Alizadehsani, R., Acharya, U. R., & D’Agostino, D. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70069 · Cited by 12

Data-driven machine learning: A new approach to process and utilize biomedical data

., K., Srivastava, A., & Jha, S. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99864-2.00017-2 · Cited by 9

Resource efficient aortic distensibility calculation by end to end spatiotemporal learning of aortic lumen from multicentre multivendor multidisease CMR images

Bohoran, T. A., Parke, K. S., Graham-Brown, M. P. M., Meisuria, M., Singh, A., Wormleighton, J. V., Adlam, D., Gopalan, D., Davies, M. J., Williams, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-48986-6 · Cited by 9

Fusing of Medical Images and Reports in Diagnostics of Brain Diseases

Vatian, A. S., Gusarova, N., Dobrenko, N., Klochkov, A., Nigmatullin, N., Lobantsev, A., & Shalyto, A. A. (2019) · doi:10.1145/3357777.3357793 · Cited by 9

Universal Adversarial Perturbations in Epileptic Seizure Detection

Aminifar, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206696 · Cited by 9

A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models

Sivaslıoğlu, S., Çatak, F. Ö., & Şahinbaş, K. (2021) · Informatica · doi:10.31449/inf.v45i1.3234 · Cited by 9

One-Pixel Attacks Against Medical Imaging: A Conceptual Framework

Sipola, T., & Kokkonen, T. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-72657-7_19 · Cited by 9

Robustness-via-synthesis: Robust training with generative adversarial perturbations

Baytaş, İ. M., & Deb, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.034 · Cited by 9

Understanding deep learning defenses against adversarial examples through visualizations for dynamic risk assessment

Echeberria-Barrio, X., Gil, A., Egana, J., & Orduna-Urrutia, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06812-y · Cited by 9

Model soups improve performance of dermoscopic skin cancer classifiers

Maron, R. C., Hekler, A., Haggenmüller, S., Kalle, C. V., Utikal, J. S., Müller, V. A., Gaiser, M. R., Meier, F. E., Hobelsberger, S., Gellrich, F. F., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.07.002 · Cited by 10

On the Adversarial Robustness of Feature Selection Using LASSO

Li, F., Lai, L., & Cui, S. R. (2020) · Machine learning for signal processing ... · doi:10.1109/mlsp49062.2020.9231631 · Cited by 9

Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 ModelsPreprint

Tripathi, A. M., & Mishra, A. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04004 · Cited by 13

Adversarial Visual Robustness by Causal InterventionPreprint

Tang, K., Tao, M., & Zhang, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09534 · Cited by 20

How Adversarial attacks affect Deep Neural Networks Detecting COVID-19?Preprint

Gougeh, R. A. (2021) · Research Square · doi:10.21203/rs.3.rs-763355/v1 · Cited by 9

Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission

Adebayo, O., Aldoori, J., Allum, W., Aruparayil, N., Badran, A., Beatty, J. W., Bhatia, S., Bolton, W., Giwa, L., Burke, J., et al. (2022) · doi:10.1308/fos2.2022 · Cited by 11

AI-Driven Healthcare Cyber-Security: Protecting Patient Data and Medical Devices

Bonagiri, K., S, N. M. V., Gopalsamy, M., Iyswariya, A., Reni Hena Helan, R., & Sultanuddin, S. J. (2024) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici62503.2024.10696183 · Cited by 8

Ultrafast Probabilistic Neuron in an Artificial Spin Ice for Robust Deep Neural Networks

Liang, Z., Bu, T., Lyu, Z., Liu, Z., Hrabec, A., Wang, L., Dou, Y., Ding, J., Ge, P., Yang, W., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202417334 · Cited by 8

Controversies in the Application of AI in Radiology—Is There Medico-Legal Support? Aspects from Romanian Practice

Ungureanu, A., Matei, S.-C., & Malita, D. C. (2025) · Diagnostics · doi:10.3390/diagnostics15020230 · Cited by 10

AtlasGPT: a language model grounded in neurosurgery with domain-specific data and document retrieval

Ali, R., Abdulrazeq, H., Patil, A., Cheatham, M., Connolly, I. D., Tang, O. Y., Doberstein, C., Riccelli, T. E., Huang, K. T., Shankar, G. M., et al. (2025) · Journal of neurosurgery · doi:10.3171/2024.12.jns241607 · Cited by 8

A survey on adversarial machine learning: Attacks, defenses, real-world applications, and future research directions

Qiao, Y., Sathyanarayana, N. B., Shi, C., He, Z., Wang, T., & Hou, T. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132670 · Cited by 9

A clinical environment simulator for dynamic AI evaluation

Luo, L., Kim, S. E., Zhang, X., Kernbach, J. M., Kenia, R., Acosta, J. N., Nathanson, L. A., Haimovich, A. D., Rodman, A. M., Goh, E., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04252-6 · Cited by 14

Adversarial Robust Training of Deep Learning MRI Reconstruction Models

Calivá, F., Cheng, K., Shah, R., & Pedoia, V. (2021) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2021-df47 · Cited by 8

Adversarial learning in quantum artificial intelligence

Shen, P.-X., Jiang, W.-J., Li, W., Lu, Z., & Deng, D.-L. (2021) · Acta Physica Sinica · doi:10.7498/aps.70.20210789 · Cited by 8

Deep anonymization of medical imaging

Fezai, L., Urruty, T., Bourdon, P., Fernández-Maloigne, C., & Initiative, A. D. N. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13686-2 · Cited by 9

When will AI misclassify? Intuiting failures on natural images

Nartker, M. S., Zhou, Z., & Firestone, C. (2023) · Journal of Vision · doi:10.1167/jov.23.4.4 · Cited by 8

Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence

Thaxton, C. T., & Dardik, A. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.05.003 · Cited by 8

Adversarial attacks and defenses using feature-space stochasticity

Ukita, J., & Ohki, K. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.022 · Cited by 8

Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis

Sufian, M. A., & Niu, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109597 · Cited by 12

Improving transferability of adversarial examples via statistical attribution-based attacks

Zhu, H., Jia, Y., Yan, Y., & Yang, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107341 · Cited by 9

Model Fooling Attacks Against Medical Imaging: A Short Survey

Sipola, T., Puuska, S., & Kokkonen, T. (2020) · Information & Security An International Journal · doi:10.11610/isij.4615 · Cited by 10

RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR

Wang, W., Tang, P., Xiong, L., & Jiang, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67667-4_7 · Cited by 8

The Clinician's Guide to the Machine Learning Galaxy

Shen, L., Kann, B. H., Taylor, R. A., & Shung, D. L. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.658583 · Cited by 8

Deep Learning in Healthcare

Belhouari, S. B., & Islam, A. (2021) · Lecture notes in bioengineering · doi:10.1007/978-3-030-67303-1_13 · Cited by 8

Dual Head Adversarial Training

Jiang, Y., Ma, X., Erfani, S., & Bailey, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533363 · Cited by 8

Seeds of SEED: NMT-Stroke: Diverting Neural Machine Translation through Hardware-based Faults

Cai, K., Chowdhuryy, M. H. I., Zhang, Z., & Yao, F. (2021) · International Symposium on Secure and Private Execution Environment Design (SEED) · doi:10.1109/seed51797.2021.00019 · Cited by 8

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

Usynin, D., Rueckert, D., Passerat‐Palmbach, J., & Kaissis, G. A. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2022-0014 · Cited by 10

Defense Against Adversarial Attacks Based on Stochastic Descent Sign Activation Networks on Medical Images

Yang, Y., Shih, F. Y., & Roshan, U. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422540052 · Cited by 8

Evaluation of neural networks defenses and attacks using NDCG and reciprocal rank metrics

Brama, H., Dery, L., & Grinshpoun, T. (2022) · International Journal of Information Security · doi:10.1007/s10207-022-00652-0 · Cited by 10

Detecting One-Pixel Attacks Using Variational Autoencoders

Alatalo, J., Sipola, T., & Kokkonen, T. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-04826-5_60 · Cited by 8

Improving transferability of adversarial examples with powerful affine-shear transformation attack

Wang, X., Huang, C., & Cheng, H. Y. (2022) · Computer Standards & Interfaces · doi:10.1016/j.csi.2022.103693 · Cited by 8

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

Liu, S., Amin-Ahmadi, B., Liu, R., Zheng, Q., & Zhang, X. (2023) · Materials Characterization · doi:10.1016/j.matchar.2023.112779 · Cited by 8

Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks

Vizcarra, J. C., Teich, A. F., Dugger, B. N., & Gutman, D. A. (2023) · Free Neuropathology · doi:10.17879/freeneuropathology-2023-4696 · Cited by 8

Explainability Matters: Backdoor Attacks on Medical ImagingPreprint

Nwadike, M. S., Miyawaki, T., Sarkar, E., Maniatakos, M., & Shamout, F. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00008 · Cited by 16

Adversarial Attack and Defense Mechanisms in Medical Imaging: A Comprehensive Review

Surekha, M., Sagar, A. K., & Khemchandani, V. (2024) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/ic2pct60090.2024.10486235 · Cited by 7

A Survey of Adversarial Examples in Computer Vision: Attack, Defense, and Beyond

Xu, K., Yajuan, L. U., Wang, Z., & Liang, C. C. (2025) · Wuhan University Journal of Natural Sciences · doi:10.1051/wujns/2025301001 · Cited by 9

Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis

Shim, J., & Jo, K. (2025) · IEEE Access · doi:10.1109/access.2025.3570898 · Cited by 7

Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods

Lee, S. B. (2025) · Scientific Reports · doi:10.1038/s41598-025-02294-3 · Cited by 8

Cyber Risks to Next-Gen Brain-Computer Interfaces: Analysis and Recommendations

Schroder, T., Sirbu, R. A., Park, S., Morley, J., Street, S., & Floridi, L. (2025) · Neuroethics · doi:10.1007/s12152-025-09607-3 · Cited by 8

FFTMed: leveraging fast-fourier transform for a lightweight and adversarial-resilient medical image segmentation framework

Pham, V. T., Ha, M. H., Bui, B. V. Q., & Hy, T. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-21799-5 · Cited by 9

From AI security to ethical AI security: a comparative risk-mitigation framework for classical and hybrid AI governance

Ilari, L., Tiribelli, S., & Caruso, F. (2026) · AI and Ethics · doi:10.1007/s43681-025-00935-x · Cited by 8

SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle

Zhang, X., Zhao, P., Jaskolka, J., Li, H., & Lu, R. (2026) · Empirical Software Engineering · doi:10.1007/s10664-025-10795-y · Cited by 8

MalAder: Decision-Based Black-Box Attack Against API Sequence Based Malware Detectors

Chen, X., Cui, L., Wen, H., Li, Z., Zhu, H., Hao, Z., & Sun, L. (2023) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58367.2023.00027 · Cited by 8

Mitigating adversarial threats in deep CT image diagnosis models via a dual-stage inference-time defense

Sheikh, B. U. H. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111909 · Cited by 9

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions

Xiao, Y., Benedict, S., Cui, Y., Glide‐Hurst, C. K., Graves, S. A., Jia, X., Kry, S. F., Li, H., Lin, L., Matuszak, M. M., et al. (2025) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2025.01.006 · Cited by 7

Exploring the Intersection of AI, Healthcare, and Environmental Sustainability: Future Trends and Challenges

Kumar, A. A., Suryadevara, R., Sowmyya, T., & Chanal, G. B. (2024) · Approaches to global sustainability, markets, and governance · doi:10.1007/978-981-97-9555-0_2 · Cited by 7

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion

Hoang, T. N., Lam, C. T., Low, B. K. H., & Jaillet, P. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7

An Adversarial sample defense method based on multi-scale GAN

Shao, M., Liu, S., Wang, R., & Zhang, G. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01374-w · Cited by 7

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Smith, E. R., & Shah, J. (2022) · Gastroenterology Insights · doi:10.3390/gastroent13030022 · Cited by 7

How Effective is Adversarial Training of CNNs in Medical Image Analysis?

Xie, Y., & Fetit, A. E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_33 · Cited by 7

Adversarial Robustness through the Lens of Convolutional Filters

Gavrikov, P., & Keuper, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00025 · Cited by 10

Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors

Kurniawan, A., Ohsita, Y., & Murata, M. (2022) · Sensors · doi:10.3390/s22228642 · Cited by 7

Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images

Matsuo, Y., & Takemoto, K. (2022) · Applied Sciences · doi:10.3390/app122412564 · Cited by 8

How to Protect Biotechnology and Biosecurity from Adversarial AI Attacks? A Global Governance Perspective

Pauwels, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-26034-6_11 · Cited by 7

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis

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