2026-04-17T07:57:35Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/2205722025-12-05T09:56:35Zcom_2072_1057col_2072_478917col_2072_478920
RECERCAT author Osuala, Richard author Lang, Daniel M. author Riess, Anneliese author Kaissis, Georgios author Szafranowska, Zuzanna author Skorupko, Grzegorz author Díaz, Oliver author Schnabel, Julia A. author Lekadir, Karim, 1977- 2025-04-24T10:13:30Z2025-04-24T10:13:30Z2025-02-11 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. Springer Nature Switzerland AG (c) Richard Osuala et al., 2025 info:eu-repo/semantics/openAccess Càncer de mama Aprenentatge automàtic Imatges per ressonància magnètica Breast cancer Machine learning Magnetic resonance imaging Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion