7 citations · 18 across the 8 of their papers we have counts for
7 papers · 1 filter
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Jonathan Fhima, Jan Van Eijgen, Lennert Beeckmans +6
Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (…
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation
Samah Khawaled, Moti Freiman
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have b…
A self-attention model for robust rigid slice-to-volume registration of functional MRI
Samah Khawaled, Simon K. Warfield, Moti Freiman
Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head…
Exploring the Interplay Between Colorectal Cancer Subtypes Genomic Variants and Cellular Morphology: A Deep-Learning Approach
Hadar Hezi, Daniel Shats, Daniel Gurevich +2
Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CR…
Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch Embeddings
Daniel Shats, Hadar Hezi, Guy Shani +2
Assessing microsatellite stability status of a patient's colorectal cancer is crucial in personalizing treatment regime. Recently, convolutional-neural-networks (CNN) combined with…
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations
Jonathan Fhima, Jan Van Eijgen, Moti Freiman +2
Introduction: For supervised deep learning (DL) tasks, researchers need a large annotated dataset. In medical data science, one of the major limitations to develop DL models is the…
Unsupervised Deep-Learning Based Deformable Image Registration: A Bayesian Framework
Samah Khawaled, Moti Freiman
Unsupervised deep-learning (DL) models were recently proposed for deformable image registration tasks. In such models, a neural-network is trained to predict the best deformation f…