82 citations · 84 across the 4 of their papers we have counts for
4 papers
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Richard Osuala, Daniel M. Lang, Anneliese Riess +6
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and…
Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models
Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska +6
While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased…
Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
Zuzanna Szafranowska, Richard Osuala, Bennet Breier +3
Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortali…
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Richard Osuala, Kaisar Kushibar, Lidia Garrucho +6
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inte…