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Osuala, R. ; Lang, D.M. ; Riess, A. ; Kaissis, G. ; Szafranowska, Z.* ; Skorupko, G.* ; Diaz, O.* ; Schnabel, J.A. ; Lekadir, K.*

Enhancing the utility of privacy-preserving cancer classification using synthetic data.

In: (Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care). Berlin [u.a.]: Springer, 2025. 54-64 (Lect. Notes Comput. Sc. ; 15451 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Breast Imaging ; Differential Privacy ; Generative Models
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15451 LNCS, Heft: , Seiten: 54-64 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)