5 citations · 13 across the 4 of their papers we have counts for
4 papers
Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Ashkan Moradi, Fadila Zerka, Joeran S. Bosma +7
Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (…
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Gabriele Lozupone, Alessandro Bria, Francesco Fontanella +3
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, fo…
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Alessa Hering, Sarah de Boer, Anindo Saha +6
The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametr…