Summary of Machine Unlearning For Medical Imaging, by Reza Nasirigerdeh et al.
Machine Unlearning for Medical Imaging
by Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel, Daniel Rueckert, Georgios Kaissis
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper evaluates different unlearning algorithms in the medical imaging domain. The goal is to remove the impact of specific training samples from a pretrained model, fulfilling the “right to be forgotten”. The study assesses the performance and computational efficiency of various unlearning methods on retain and forget sets, demonstrating their effectiveness but also highlighting potential biases against certain sample types or sizes. Additionally, the algorithms require additional computational overhead for hyperparameter tuning. While machine unlearning shows promise for medical imaging applications, further improvements are needed to make it practical. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine unlearning is a way to remove the impact of specific training samples from a model. This helps people, like patients, control what information their data contributes to models. The researchers tested different ways to do this in medical images and found that they work well for some types of samples but not others. They also found that these methods can be biased against certain types of samples or need extra computing power. Overall, machine unlearning could be helpful for medical imaging, but more work is needed. |
Keywords
* Artificial intelligence * Hyperparameter
