176 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Marquette UniversityUS6 papers
- Indiana University School of Medicine3 papers
- Queen Mary University of LondonGB3 papers
- The University of SydneyAU3 papers
- University Hospital BonnDE3 papers
- University of MichiganUS3 papers
- University of TorontoCA3 papers
- Aichi Medical UniversityJP2 papers
- Artificial Intelligence in Medicine (Canada)CA2 papers
- Baylor College of MedicineUS2 papers
- Bostwick LaboratoriesUS2 papers
- Cleveland ClinicUS2 papers
6 papers · 1 filter
Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
Juan Liu, Kevin M. Koch
Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM i…
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
Peter Ström, Kimmo Kartasalo, Henrik Olsson +29
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-…
A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images
Yunzhe Xue, Meiyan Xie, Fadi G. Farhat +5
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities co…
A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images
Yunzhe Xue, Fadi G. Farhat, Olga Boukrina +4
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. We prop…
Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution
Juan Liu, Andrew S. Nencka, L. Tugan Muftuler +3
Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-…
Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning
Andrew S. Nencka, Andrew Klein, Kevin M. Koch +7
Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 dec…