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Enhancing adversarial defense for medical image analysis systems with pruning and attention mechanism

https://doi.org/10.1002/mp.15208
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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CBAM: Convolutional Block Attention Module
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Adversarial radiomics: the rising of potential risks in medical imaging from adversarial learning
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Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures
inferredcites →https://doi.org/10.1007/s11263-019-01228-7openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1016/j.media.2017.07.005openalex-snapshot
A survey on deep learning in medical image analysis
inferredcites →https://doi.org/10.1016/j.patcog.2020.107332openalex-snapshot
Understanding adversarial attacks on deep learning based medical image analysis systems
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/globalsip.2018.8646578openalex-snapshot
Defending DNN Adversarial Attacks with Pruning and Logits Augmentation
inferredcites →https://doi.org/10.1109/iccv.2017.541openalex-snapshot
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
inferredcites →https://doi.org/10.1109/icmcis.2019.8842663openalex-snapshot
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
inferredcites →https://doi.org/10.1109/isbi.2018.8363556openalex-snapshot
A new CNN-based system for early diagnosis of prostate cancer
inferredcites →https://doi.org/10.1109/isbi.2019.8759540openalex-snapshot
Radiomic-Based Framework for Early Diagnosis of Lung Cancer
inferredcites →https://doi.org/10.1109/isbi.2019.8759548openalex-snapshot
Exploiting Visual and Report-Based Information for Chest X-RAY Analysis by Jointly Learning Visual Classifiers and Topic Models
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tip.2019.2940533openalex-snapshot
Image Super-Resolution as a Defense Against Adversarial Attacks
inferredcites →https://doi.org/10.1109/tpami.2020.2978474openalex-snapshot
Deeply Supervised Discriminative Learning for Adversarial Defense
inferredcites →https://doi.org/10.1145/3357384.3358150openalex-snapshot
Adversarial Structured Neural Network Pruning
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1506.02626openalex-snapshot
Learning both Weights and Connections for Efficient Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.1801.00553openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1803.01442openalex-snapshot
Stochastic Activation Pruning for Robust Adversarial Defense
inferredcites →https://doi.org/10.48550/arxiv.1909.10147openalex-snapshot
Robust Local Features for Improving the Generalization of Adversarial Training
inferredcites →https://doi.org/10.48550/arxiv.1911.10291openalex-snapshot
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
inferredcites →https://doi.org/10.48550/arxiv.2009.08233openalex-snapshot
Label Smoothing and Adversarial Robustness

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?
inferred← citeshttps://doi.org/10.1007/978-3-032-13800-2_4openalex-snapshot
Towards Better Adversarial Defense on Lung CT Nodule Classification Models with Feature Enhancement
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A learning-based robust framework for chest x-ray pneumonia classification: Privacy-preserving deep learning with adversarial training
inferred← citeshttps://doi.org/10.1038/s41598-026-74077-3openalex-snapshot
Image-embedded prompt injection vulnerability of vision-language models in dental radiology: a cross-vendor attack–defense evaluation
inferred← citeshttps://doi.org/10.1109/aisummit66170.2025.11411577openalex-snapshot
Enhancing Lung and Colon Cancer Histopathological Image Classification through Gaussian Defense Under Adversarial Perturbations
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Cancer Medical Image Analysis Across Modalities Using Attention Mechanism and Capsule Network
inferred← citeshttps://doi.org/10.1109/iconstem56934.2023.10142395openalex-snapshot
Evasion and Poison attacks on Logistic Regression-based Machine Learning Classification Model
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Global Attention Mechanism–Enhanced Deep Learning for Multi‐Sequence MRI Diagnosis of Early Femoral Head Osteonecrosis
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Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
inferred← citeshttps://doi.org/10.1371/journal.pone.0307363openalex-snapshot
Auto encoder-based defense mechanism against popular adversarial attacks in deep learning
inferred← citeshttps://doi.org/10.3390/math11204272openalex-snapshot
A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense
inferred← citeshttps://doi.org/10.4018/406036openalex-snapshot
AI Safety
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Hierarchical Adversarial Policy Learning for Robust and Explainable Defence in Ophthalmic Artificial Intelligence Systems
inferred← citeshttps://doi.org/10.53941/ijndi0201006openalex-snapshot
Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds
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AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics
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