Authors: Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir
Abstract: Deep learning holds immense promise for aiding radiologists in breast cancer
detection. However, achieving optimal model performance is hampered by
limitations in availability and sharing of data commonly associated to patient
privacy concerns. Such concerns are further exacerbated, as traditional deep
learning models can inadvertently leak sensitive training information. This
work addresses these challenges exploring and quantifying the utility of
privacy-preserving deep learning techniques, concretely, (i) differentially
private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training
data generated by our proposed malignancy-conditioned generative adversarial
network. We assess these methods via downstream malignancy classification of
mammography masses using a transformer model. Our experimental results depict
that synthetic data augmentation can improve privacy-utility tradeoffs in
differentially private model training. Further, model pretraining on synthetic
data achieves remarkable performance, which can be further increased with
DP-SGD fine-tuning across all privacy guarantees. With this first in-depth
exploration of privacy-preserving deep learning in breast imaging, we address
current and emerging clinical privacy requirements and pave the way towards the
adoption of private high-utility deep diagnostic models. Our reproducible
codebase is publicly available at https://github.com/RichardObi/mammo_dp.
Source: http://arxiv.org/abs/2407.12669v1
