Y. Yoo

发表

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

Lisa Tang, Youngjin Yoo, Roger C. Tam, 2016, IEEE Transactions on Medical Imaging.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

Dorin Comaniciu, Thomas Flohr, Zhoubing Xu, 2020, Radiology. Artificial intelligence.

Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS

Ravi S. Menon, C. Crainiceanu, D. Arnold, 2019, Neurology.

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

C. Granziera, M. Weigel, L. Kappos, 2020, NeuroImage: Clinical.

Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9‐10 Year‐Old Children

Adam V. Dvorak, C. Beaulieu, L. Siegel, 2020, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

Rapid myelin water imaging in human cervical spinal cord.

Anthony Traboulsee, Roger Tam, Irene Vavasour, 2016, Magnetic resonance in medicine.

Self-supervised Learning from 100 Million Medical Images pdf

Dorin Comaniciu, Sasa Grbic, James M. Balter, 2022, ArXiv.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

Dorin Comaniciu, Thomas Flohr, Sasa Grbic, 2020, European Radiology.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls

Roger C. Tam, Tom Brosch, Anthony Traboulsee, 2017, NeuroImage: Clinical.

3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19 pdf

Youngjin Yoo, Zhoubing Xu, Sasa Grbic, 2020, ArXiv.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

Thomas J. Re, D. Comaniciu, H. Meyer, 2021, Scientific Reports.

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

Youngjin Yoo, Mannudeep K. Kalra, Eli Gibson, 2020, Medical Image Anal..

Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

Lisa Tang, Youngjin Yoo, Roger C. Tam, 2015, MICCAI.

Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images

Eli Gibson, James M. Balter, Yue Cao, 2021, Journal of medical imaging.

Rapid myelin water imaging in human cervical spinal cord

Anthony Traboulsee, Roger Tam, Irene Vavasour, 2016, Magnetic resonance in medicine.

Deep learning of brain images and its application to multiple sclerosis

Tom Brosch, Y. Yoo, L.Y.W. Tang, 2016 .

Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome

Youngjin Yoo, Anthony Traboulsee, Shannon H. Kolind, 2019, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections

Julien Cohen-Adad, Lisa Tang, Youngjin Yoo, 2017, DLMIA/ML-CDS@MICCAI.

Exploring the Contribution of Myelin Content in Normal Appearing White Matter to Cognitive Outcomes in Cerebral Small Vessel Disease.

Rachel A. Crockett, A. MacKay, C. Laule, 2021, Journal of Alzheimer's disease : JAD.

The Quantitative 18-fluorodeoxyglucose PET Study in the Differential Diagnosis between Idiopathic Parkinson's Disease and Multiple System Atrophy.

Y. Yoo, C. Lyoo, M. Lee, 2006 .

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Thomas J. Re, D. Comaniciu, Y. Lui, 2022, Radiology. Artificial intelligence.

Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning

Youngjin Yoo, Roger C. Tam, Tom Brosch, 2014, MICCAI.

Contrastive self-supervised learning from 100 million medical images with optional supervision

Florin C. Ghesu, Pragneshkumar B. Patel, D. Comaniciu, 2022, Journal of medical imaging.

3D-2D GAN based brain metastasis synthesis with configurable parameters for fully 3D data augmentation

Thomas J. Re, D. Kondziolka, D. Comaniciu, 2023, Medical Imaging.

Deep learning for feature discovery in brain MRIs for patient-level classification with applications to multiple sclerosis : [errata]

Youngjin Yoo, Y. Yoo, 2018 .

Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans

Dorin Comaniciu, Eli Gibson, Filiz Bunyak, 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction

Lisa Tang, Xingtong Liu, Youngjin Yoo, 2016, MICCAI.

Quantification of Tomographic Patterns associated with COVID-19 from Chest CT

Thomas J. Re, P. Grenier, D. Comaniciu, 2020, ArXiv.

Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis

Lisa Tang, Youngjin Yoo, Roger C. Tam, 2016, LABELS/DLMIA@MICCAI.

Non-Local Spatial Regularization of MRI T2 Relaxation Images for Myelin Water Quantification

Youngjin Yoo, Roger C. Tam, Y. Yoo, 2013, MICCAI.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls

A. MacKay, I. Vavasour, A. Traboulsee, 2017, NeuroImage: Clinical.