375 citations · 878 across the 24 of their papers we have counts for
9 papers · 1 filter
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
Lidia Garrucho, Smriti Joshi, Kaisar Kushibar +43
Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imagin…
Single Image Test-Time Adaptation via Multi-View Co-Training
Smriti Joshi, Richard Osuala, Lidia Garrucho +4
Test-time adaptation enables a trained model to adjust to a new domain during inference, making it particularly valuable in clinical settings where such on-the-fly adaptation is re…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla +11
The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, a…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
Karim Lekadir, Richard Osuala, Catherine Gallin +13
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI s…
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
Jose Bernal, Kaisar Kushibar, Mariano Cabezas +3
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diag…
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Jose Bernal, Kaisar Kushibar, Daniel S. Asfaw +4
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, det…
Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features
Kaisar Kushibar, Sergi Valverde, Sandra Gonzalez-Villa +4
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time because morphological changes in t…