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
42 papers
Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
Pouya Afshin, David Helminiak, Tongtong Lu +5
Breast-conserving surgery (BCS) aims to completely remove malignant lesions while maximizing healthy tissue preservation. Intraoperative margin assessment is essential to achieve a…
Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvement
K M Sajjadul Islam, Ravi Teja Karri, Srujan Vegesna +2
Understanding patient feedback is crucial for improving healthcare services, yet analyzing unlabeled short-text feedback presents challenges due to limited data and domain-specific…
Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model
Sepehr Salem Ghahfarokhi, Tyrell To, Julie Jorns +3
Data limitation is a significant challenge in applying deep learning to medical images. Recently, the diffusion probabilistic model (DPM) has shown the potential to generate high-q…
Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy
Moo K. Chung, Ji Bi Che, Veena A. Nair +7
We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe…
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Junhao Wen, Mathilde Antoniades, Zhijian Yang +4
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neu…
Rapid Estimation of Left Ventricular Contractility with a Physics-Informed Neural Network Inverse Modeling Approach
Ehsan Naghavi, Haifeng Wang, Lei Fan +4
Physics-based computer models based on numerical solution of the governing equations generally cannot make rapid predictions, which in turn, limits their applications in the clinic…
Minding rights: Mapping ethical and legal foundations of 'neurorights'
Sjors Ligthart, Marcello Ienca, Gerben Meynen +17
The rise of neurotechnologies, especially in combination with AI-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental…
Unconstrained Kinematic MRI Tracking of Wrist Carpal Bones
Mohammad Zarenia, Volkan Emre Arpinar, Andrew S. Nencka +2
In this preliminary study technical methodology for kinematic tracking and profiling of wrist carpal bones during unconstrained movements is explored. Heavily under-sampled and fat…
Nonparametric Bayesian Knockoff Generators for Feature Selection Under Complex Data Structure
Michael J. Martens, Anjishnu Banerjee, Xinran Qi +1
The recent proliferation of high-dimensional data, such as electronic health records and genetics data, offers new opportunities to find novel predictors of outcomes. Presented wit…
Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams
Andrew S. Nencka, Mohammad Sherafati, Timothy Goebel +2
Purpose: This study evaluates the effectiveness and impact of automated order-based protocol assignment for magnetic resonance imaging (MRI) exams using natural language processing…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used
L. Tugan Muftuler, Andrew S. Nencka, Kevin M. Koch
Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Si…
Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data
Alan D. Kaplan, Qi Cheng, K. Aditya Mohan +12
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflict…
Reducing the Dimensionality of Optimal Experiment Design for Magnetic Resonance Fingerprinting
Nikolai J. Mickevicius, Andrew S. Nencka, Eric S. Paulson
Nuclear magnetic resonance signal dynamics as described by the Bloch equations are highly complex and often are without closed form solutions. This is especially the case for quant…
Isoscalar and isovector giant resonances in 44Ca, 54Fe, 64,68Zn and 56,58,60,68Ni
G. Bonasera, S. Shlomo, D. H. Youngblood +3
We have studied the uncharacteristic behavior of the measured values of the isoscalar and isovector centroid energies, ECEN, of giant resonances with multipolarity L=0-3 in 44Ca, 5…
Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping
Juan Liu, Kevin M Koch
Quantitative susceptibility mapping (QSM) utilizes MRI phase information to estimate tissue magnetic susceptibility. The generation of QSM requires solving ill-posed background fie…
Optimization of hyperparameters for SMS reconstruction
L. Tugan Muftuler, Volkan Emre Arpinar, Kevin Koch +5
Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical…
Model-based Learning for Quantitative Susceptibility Mapping
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements…
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga +17
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems…
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-…