{"total":62,"rows":[{"id":"2111.02398","title":"Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods","created":"2021-11-01","cited_by_count":83,"area":null},{"id":"2105.00203","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","created":"2021-05-01","cited_by_count":52,"area":null},{"id":"2204.10806","title":"A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity","created":"2022-04-22","cited_by_count":23,"area":null},{"id":"2402.02160","title":"Data Poisoning for In-context Learning","created":"2024-02-03","cited_by_count":21,"area":"safety"},{"id":"2103.03438","title":"Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack","created":"2021-03-05","cited_by_count":15,"area":null},{"id":"2112.02625","title":"Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View","created":"2021-12-05","cited_by_count":15,"area":null},{"id":"2310.04780","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","created":"2023-10-07","cited_by_count":14,"area":null},{"id":"2102.04150","title":"Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples","created":"2021-02-08","cited_by_count":13,"area":null},{"id":"2206.03353","title":"Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples","created":"2022-06-07","cited_by_count":11,"area":null},{"id":"2203.17081","title":"Interpretation of Black Box NLP Models: A Survey","created":"2022-03-31","cited_by_count":9,"area":null},{"id":"2102.02729","title":"Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review","created":"2021-02-04","cited_by_count":8,"area":null},{"id":"2109.11125","title":"Adversarial Transfer Attacks With Unknown Data and Class Overlap","created":"2021-09-23","cited_by_count":8,"area":null},{"id":"2105.01959","title":"Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning","created":"2021-05-05","cited_by_count":7,"area":null},{"id":"2106.08153","title":"Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology","created":"2021-06-14","cited_by_count":7,"area":null},{"id":"2106.07141","title":"Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks","created":"2021-06-14","cited_by_count":6,"area":null},{"id":"2503.08801","title":"Enhanced Estimation Techniques for Certified Radii in Randomized Smoothing","created":"2025-03-11","cited_by_count":6,"area":null},{"id":"2212.14177","title":"Current State of Community-Driven Radiological AI Deployment in Medical Imaging","created":"2022-12-29","cited_by_count":5,"area":null},{"id":"2311.06361","title":"CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization","created":"2023-11-10","cited_by_count":5,"area":null},{"id":"2106.15357","title":"Multi-stage Optimization based Adversarial Training","created":"2021-06-26","cited_by_count":4,"area":null},{"id":"2109.00202","title":"Federated Learning: Issues in Medical Application","created":"2021-09-01","cited_by_count":4,"area":null},{"id":"2110.13980","title":"Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions","created":"2021-10-26","cited_by_count":4,"area":null},{"id":"2201.08661","title":"The Security of Deep Learning Defences for Medical Imaging","created":"2022-01-21","cited_by_count":4,"area":null},{"id":"2208.01853","title":"Robust Graph Neural Networks using Weighted Graph Laplacian","created":"2022-08-03","cited_by_count":4,"area":null},{"id":"2508.01845","title":"Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems","created":"2025-08-03","cited_by_count":4,"area":null},{"id":"2101.02689","title":"The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks","created":"2021-01-07","cited_by_count":3,"area":null},{"id":"2102.02885","title":"Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images","created":"2021-02-04","cited_by_count":3,"area":null},{"id":"2212.08130","title":"On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices","created":"2022-12-15","cited_by_count":3,"area":null},{"id":"2312.01679","title":"Adversarial Medical Image with Hierarchical Feature Hiding","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2407.11091","title":"SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks","created":"2024-07-14","cited_by_count":3,"area":null},{"id":"2502.05214","title":"CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models","created":"2025-02-04","cited_by_count":3,"area":null},{"id":"2108.08972","title":"Application of Adversarial Examples to Physical ECG Signals","created":"2021-08-20","cited_by_count":2,"area":null},{"id":"2301.01885","title":"Enhancement attacks in biomedical machine learning","created":"2023-01-05","cited_by_count":2,"area":null},{"id":"2401.17136","title":"Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems","created":"2024-01-30","cited_by_count":2,"area":null},{"id":"2403.19444","title":"Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays","created":"2024-03-28","cited_by_count":2,"area":null},{"id":"2409.07415","title":"SoK: Security and Privacy Risks of Healthcare AI","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2410.20284","title":"Classification under strategic adversary manipulation using pessimistic bilevel optimisation","created":"2024-10-26","cited_by_count":2,"area":null},{"id":"2505.02971","title":"Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation","created":"2025-05-05","cited_by_count":2,"area":"mllm"},{"id":"2508.17215","title":"How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG System","created":"2025-08-24","cited_by_count":2,"area":"mllm"},{"id":"2104.00139","title":"Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images","created":"2021-03-31","cited_by_count":1,"area":null},{"id":"2111.10969","title":"Medical Aegis: Robust adversarial protectors for medical images","created":"2021-11-22","cited_by_count":1,"area":null},{"id":"2202.07342","title":"Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness","created":"2022-02-15","cited_by_count":1,"area":null},{"id":"2301.07520","title":"Adversarial AI in Insurance: Pervasiveness and Resilience","created":"2023-01-17","cited_by_count":1,"area":null},{"id":"2407.17587","title":"S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks","created":"2024-07-23","cited_by_count":1,"area":null},{"id":"2408.08489","title":"DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies","created":"2024-08-16","cited_by_count":1,"area":null},{"id":"2107.03620","title":"Predicting Disease Progress with Imprecise Lab Test Results","created":"2021-07-08","cited_by_count":0,"area":null},{"id":"2108.10217","title":"Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks","created":"2021-08-23","cited_by_count":0,"area":null},{"id":"2112.01777","title":"Attack-Centric Approach for Evaluating Transferability of Adversarial Samples in Machine Learning Models","created":"2021-12-03","cited_by_count":0,"area":null},{"id":"2210.17140","title":"Scoring Black-Box Models for Adversarial Robustness","created":"2022-10-31","cited_by_count":0,"area":null},{"id":"2212.06822","title":"Adversarial Attacks and Defences for Skin Cancer Classification","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2212.10565","title":"Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification","created":"2022-12-10","cited_by_count":0,"area":null},{"id":"2309.06377","title":"Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection","created":"2023-09-08","cited_by_count":0,"area":null},{"id":"2402.05713","title":"Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations","created":"2024-02-08","cited_by_count":0,"area":null},{"id":"2402.07506","title":"NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2403.14489","title":"Adversary-Robust Graph-Based Learning of WSIs","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2411.17959","title":"Adversarial Training in Low-Label Regimes with Margin-Based Interpolation","created":"2024-11-27","cited_by_count":0,"area":null},{"id":"2505.24654","title":"Black-box Adversarial Attacks on CNN-based SLAM Algorithms","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2506.15028","title":"Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2509.22113","title":"Countering adversarial evasion in regression analysis","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2510.03254","title":"Adversarial training with restricted data manipulation","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2511.00849","title":"Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2601.02562","title":"CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2608.13296","title":"Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks","created":"2026-08-13","cited_by_count":0,"area":null}],"next_cursor":null}