ArticleNature communications2022
Adversarial attacks and adversarial robustness in computational pathology.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 104 citations in OpenAlex.
- GA2AD: a generalized adaptive adversarial training framework considering surrounding hybrid risk field for autonomous driving.Nature communications · 2026Article
- Universal and transferable attacks on pathology foundation models using microscopic perturbations.Light, science & applications · 2026Article
- Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier.Journal of imaging informatics in medicine · 2026Article
- Will your next surgeon be a robot? Autonomy and AI in robotic surgery.Science robotics · 2025Review
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.Bioinformatics (Oxford, England) · 2025Article
- Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging.Medical & biological engineering & computing · 2025Article
- The inherent adversarial robustness of analog in-memory computing.Nature communications · 2025Article
- Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis.JHEP reports : innovation in hepatology · 2025Article
- Prompt injection attacks on vision language models in oncology.Nature communications · 2025Article
- Genomic privacy and security in the era of artificial intelligence and quantum computing.Discover computing · 2025Review
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Explainable AI for computational pathology identifies model limitations and tissue biomarkers.ArXiv · 2024Article
- Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.Scientific reports · 2024Article
- Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.BMC medical informatics and decision making · 2024Article
- Article
- Concepts and applications of digital twins in healthcare and medicine.Patterns (New York, N.Y.) · 2024Review
- Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma.BMC cancer · 2024Article
- [Artificial intelligence in kidney transplant pathology].Pathologie (Heidelberg, Germany) · 2024Review
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.