54 citations · 239 across the 30 of their papers we have counts for
5 papers · 1 filter
medigan: a Python library of pretrained generative models for medical image synthesis
Richard Osuala, Grzegorz Skorupko, Noussair Lazrak +9
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging. However, there is (1) limited ava…
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Lidia Garrucho, Kaisar Kushibar, Richard Osuala +7
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance sinc…
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…
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
Kaisar Kushibar, Víctor Manuel Campello, Lidia Garrucho Moras +3
Uncertainty estimation in deep learning has become a leading research field in medical image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Mo…
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study
Lidia Garrucho, Kaisar Kushibar, Socayna Jouide +3
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural netwo…