Summary of Re-identification From Histopathology Images, by Jonathan Ganz et al.
Re-identification from histopathology images
by Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Deep learning algorithms have shown promise in analyzing histopathology images for tumor subtyping and primary origin identification. However, these models require large datasets, which must be anonymized to prevent patient identity leaks. This study demonstrates that even simple deep learning algorithms can re-identify patients with substantial accuracy. The authors evaluated their algorithm on two TCIA lung cancer datasets (LSCC and LUAD) and an in-house meningioma dataset, achieving F1 scores of 50.16%, 52.30%, and 62.31% respectively. The study also proposes a risk assessment scheme to estimate the risk to patient privacy prior to publication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning algorithms are good at analyzing medical images like tumor pictures. But these images need to be made anonymous so patients aren’t identified. This study shows that even simple algorithms can still figure out who the patient is with some accuracy. The researchers tested their algorithm on lung cancer and brain tumor images, getting decent results. They also came up with a way to measure how much of a risk there is to patient privacy. |
Keywords
» Artificial intelligence » Deep learning
