mlsanityData to 7 Oct 2026

Adversarially Robust Feature Learning for Breast Cancer Diagnosis

arXiv 2402.08768 ↗First posted 13 Feb 2024eess.IV cs.LGcited by 3

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Abstract (arXiv)

Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robust feature learning (ARFL) method for a real-world application of breast cancer diagnosis. ARFL facilitates adversarial training using both standard data and adversarial data, where a feature correlation measure is incorporated as an objective function to encourage learning of robust features and restrain spurious features. To show the effects of ARFL in breast cancer diagnosis, we built and evaluated diagnosis models using two independent clinically collected breast imaging datasets, comprising a total of 9,548 mammogram images. We performed extensive experiments showing that our method outperformed several state-of-the-art methods and that our method can enhance safer breast cancer diagnosis against adversarial attacks in clinical settings.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 42 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

32 of 42 references.

References · 32

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  6. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
  7. 2016Adversarial Machine Learning at Scalecited by 934
  8. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  9. 2018Robustness May Be at Odds with Accuracycited by 873
  10. 2019Adversarial Training for Free!cited by 638
  11. 2020Overfitting in adversarially robust deep learningcited by 504
  12. 2019Adversarial Examples Improve Image Recognitioncited by 283
20 more
  1. 2019Domain-Specific Batch Normalization for Unsupervised Domain Adaptationcited by 197
  2. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 151
  3. 2020Understanding and Mitigating the Tradeoff Between Robustness and Accuracycited by 148
  4. 2019Adversarial Training Can Hurt Generalizationcited by 131
  5. 2020Feature Purification: How Adversarial Training Performs Robust Deep Learningcited by 107
  6. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  7. 2019Interpreting Adversarially Trained Convolutional Neural Networkscited by 75
  8. 2018Improving the Generalization of Adversarial Training with Domain Adaptationcited by 48
  9. 2020Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attackscited by 45
  10. 2022A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologiescited by 44
  11. 2015A Unified Gradient Regularization Family for Adversarial Examplescited by 43
  12. 2019CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learningcited by 31
  13. 2019Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approachcited by 27
  14. 2020Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detectioncited by 21
  15. 2021Adversarial Robustness via Fisher-Rao Regularizationcited by 19
  16. 2023Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challengescited by 16
  17. 2020Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalizationcited by 12
  18. 2022Why adversarial training can hurt robust accuracycited by 12
  19. 2021Stabilized Medical Image Attackscited by 10
  20. 2022Understanding the Impact of Adversarial Robustness on Accuracy Disparitycited by 9

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Imagescited by 4
  2. 2024RCDM: Enabling Robustness for Conditional Diffusion Modelcited by 0
  3. 2025Cross-Attention Multimodal Fusion for Breast Cancer Diagnosis: Integrating Mammography and Clinical Data with Explainabilitycited by 0