Summary of Enhancing the Utility Of Privacy-preserving Cancer Classification Using Synthetic Data, by Richard Osuala et al.
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
by Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper explores the application of privacy-preserving deep learning techniques to aid radiologists in breast cancer detection. The main challenge is the limited availability and sharing of data due to patient privacy concerns, which can also lead to traditional deep learning models inadvertently leaking sensitive training information. To address this, the authors investigate two approaches: differentially private stochastic gradient descent (DP-SGD) and synthetic training data generated by a malignancy-conditioned generative adversarial network. The methods are assessed through downstream malignancy classification of mammography masses using a transformer model. The results show that synthetic data augmentation can improve privacy-utility tradeoffs in DP-SGD, while pretraining on synthetic data achieves remarkable performance with fine-tuning across all privacy guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to use computers to help doctors detect breast cancer more accurately without sharing patient information. Doctors need help analyzing mammography images to find tumors early, but they can’t share the images because it’s personal and private. The authors found two ways to solve this problem: one way uses a special type of training that keeps the information secret, and another way creates fake images that are similar to real ones. They tested these methods by having a computer analyze images and found that using fake images worked really well. This means doctors might be able to get better at detecting breast cancer without sharing patient info. |
Keywords
» Artificial intelligence » Classification » Deep learning » Fine tuning » Generative adversarial network » Pretraining » Stochastic gradient descent » Synthetic data » Transformer
