Author

D. Bounias

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Preprint Sep 2026

nnFoundation: 3D Foundation Models for Radiology

Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise more transferable and data-efficient solutions, but existing approaches are limited in scale, evaluated narrowly, and often assume that a s...

C. U. Harsy, Tassilo Wald, Karol Gotkowski et al. · 0 citations

#artificial intelligence Open access Mar 2026

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction

The MAMA-MIA Challenge provides standardized datasets, evaluation protocols, and public resources to promote the development of robust and equitable artificial intelligence systems for breast cancer imaging.

Lidia Garrucho, Smriti Joshi, Kaisar Kushibar et al. · IEEE Transactions on Medical... · 2 citations

Jul 2026

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Substantial portions of report text are recoverable from FL gradients even at larger batch sizes and with domain-specific tokenizers, and safeguards such as secure aggregation and differential privacy are likely necessary to meet HIPAA and GDPR requirements for FL in radiology NLP.

Santhosh Parampottupadam, Andres Martinez, D. Bounias et al. · arXiv.org · 0 citations