3 citations · 9 across the 5 of their papers we have counts for
5 papers
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Davood Karimi, Lana Vasung, Camilo Jaimes +4
Multi-compartment modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the…
FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points
Ahmed H. Shahin, Prateek Munjal, Ling Shao +1
Semantic segmentation from user inputs has been actively studied to facilitate interactive segmentation for data annotation and other applications. Recent studies have shown that e…
Towards Robust and Reproducible Active Learning Using Neural Networks
Prateek Munjal, Nasir Hayat, Munawar Hayat +2
Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be p…
Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network
Shadab Khan, Ahmed H. Shahin, Javier Villafruela +2
To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model tha…
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus +1
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based m…