2 citations · 4 across the 3 of their papers we have counts for
5 papers
Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
Rafael Garcia-Dias, Alexandre Triay Bagur, Chayanin Tangwiriyasakul +20
Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept s…
Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
Pascaline André, Charles Heitz, Evangelia Christodoulou +10
Performance uncertainty quantification is essential for reliable validation and eventual clinical translation of medical imaging artificial intelligence (AI). Confidence intervals…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
Test-time Unsupervised Domain Adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre +3
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain…