41 citations · 43 across the 5 of their papers we have counts for
5 papers
MAIS: Memory-Attention for Interactive Segmentation
Mauricio Orbes-Arteaga, Oeslle Lucena, Sabastien Ourselin +1
Interactive medical segmentation reduces annotation effort by refining predictions through user feedback. Vision Transformer (ViT)-based models, such as the Segment Anything Model…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Enhancing Fiber Orientation Distributions using convolutional Neural Networks
Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia +4
Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from sp…
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Oeslle Lucena, Roberto Souza, Leticia Rittner +2
Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to…
Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping
Oeslle Lucena, Roberto Souza, Letícia Rittner +2
The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-s…