Papers
Topics
Authors
Recent
Search
2000 character limit reached

Erase to Enhance: Data-Efficient Machine Unlearning in MRI Reconstruction

Published 24 May 2024 in eess.IV, cs.CV, and cs.LG | (2405.15517v2)

Abstract: Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases. Although unlearning has been shown in, e.g., classification and recommendation systems, its potential in medical image-to-image translation, specifically in image recon-struction, has not been thoroughly investigated. This paper shows that machine unlearning is possible in MRI tasks and has the potential to benefit for bias removal. We set up a protocol to study how much shared knowledge exists between datasets of different organs, allowing us to effectively quantify the effect of unlearning. Our study reveals that combining training data can lead to hallucinations and reduced image quality in the reconstructed data. We use unlearning to remove hallucinations as a proxy exemplar of undesired data removal. Indeed, we show that machine unlearning is possible without full retraining. Furthermore, our observations indicate that maintaining high performance is feasible even when using only a subset of retain data. We have made our code publicly accessible.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. Scan-specific robust artificial-neural-networks for k-space interpolation (raki) reconstruction: Database-free deep learning for fast imaging. Magnetic resonance in medicine, 81(1):439–453, 2019.
  2. On instabilities of deep learning in image reconstruction and the potential costs of ai. Proceedings of the National Academy of Sciences, 117(48):30088–30095, 2020.
  3. On hallucinations in tomographic image reconstruction. IEEE transactions on medical imaging, 40(11):3249–3260, 2021.
  4. Forget unlearning: Towards true data-deletion in machine learning. In International Conference on Machine Learning, pages 6028–6073. PMLR, 2023.
  5. Measuring robustness in deep learning based compressive sensing. In International Conference on Machine Learning, pages 2433–2444. PMLR, 2021.
  6. Deep learning-based unlearning of dataset bias for mri harmonisation and confound removal. NeuroImage, 228:117689, 2021.
  7. Erasing concepts from diffusion models. arXiv preprint arXiv:2303.07345, 2023.
  8. Making ai forget you: Data deletion in machine learning. Advances in neural information processing systems, 32, 2019.
  9. Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11516–11524, 2021.
  10. Federated unlearning: How to efficiently erase a client in fl? arXiv preprint arXiv:2207.05521, 2022.
  11. Neural networks memorise personal information from one sample. Scientific Reports, 13(1):21366, 2023.
  12. Model sparsification can simplify machine unlearning. arXiv preprint arXiv:2304.04934, 2023.
  13. fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning. Radiology: Artificial Intelligence, 2(1):e190007, 2020.
  14. Machine unlearning for image-to-image generative models. arXiv preprint arXiv:2402.00351, 2024.
  15. Have you forgotten? a method to assess if machine learning models have forgotten data. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23, pages 95–105. Springer, 2020.
  16. Descent-to-delete: Gradient-based methods for machine unlearning. In Algorithmic Learning Theory, pages 931–962. PMLR, 2021.
  17. A survey of machine unlearning. arXiv preprint arXiv:2209.02299, 2022.
  18. A deep cascade of convolutional neural networks for dynamic mr image reconstruction. IEEE transactions on Medical Imaging, 37(2):491–503, 2017.
  19. End-to-end variational networks for accelerated mri reconstruction. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23, pages 64–73. Springer, 2020.
  20. Why patient data cannot be easily forgotten? In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 632–641. Springer, 2022.
  21. Fast yet effective machine unlearning. IEEE Transactions on Neural Networks and Learning Systems, 2023.
  22. Unshuffling data for improved generalization in visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1417–1427, 2021.
  23. Accelerating magnetic resonance imaging via deep learning. In 2016 IEEE 13th international symposium on biomedical imaging (ISBI), pages 514–517. IEEE, 2016.
  24. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
  25. Machine unlearning of features and labels. arXiv preprint arXiv:2108.11577, 2021.
  26. Machine unlearning: A survey. ACM Computing Surveys, 56(1):1–36, 2023.
  27. fastmri: An open dataset and benchmarks for accelerated mri. arXiv preprint arXiv:1811.08839, 2018.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 2 tweets with 0 likes about this paper.

Sign Up for Free