Impact of differential privacy on breast ultrasound image classification performance using vision transformer.

ABSTRACT:

Background

Breast ultrasound images contain highly sensitive anatomical and diagnostic information. Deep learning (DL) classifiers trained on such data can inadvertently memorize patient-specific features, creating privacy leakage even when identifiers are removed. Differential privacy (DP) offers a mathematically rigorous bind on this leakage, yet the scarcity of public breast ultrasound datasets and their markedly different imaging characteristics mean that the impact of DP on diagnostic accuracy remains insufficiently quantified. We therefore conducted a systematic experimental assessment of the privacy-utility trade-off to generate parameter-specific evidence for clinical deployment.

Methods

We curated a 2,149-image breast ultrasound dataset (1,289 benign, 860 malignant)

SUBMITTER: Yang J 

PROVIDER: S-EPMC12682540 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature