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Assessment of the potential of a Deep Learning Knee Segmentation and Anomaly Detection Tool in the clinical routine

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Characterizing Knee Osteoarthritis Progression with Structural Phenotypes using MRI and Deep Learning

Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Quantification (MSQ-Net) of Knee from UTE MRIs

Deep Learning Improves Detection of Anterior Cruciate Ligament- and Meniscus Tear Detection in Knee MRI

Deep Learning Reconstruction of 3D Zero Echo Time Magnetic Resonance Images for the Creation of 3D Printed Anatomic Models

Deep-Learning Based Image Reconstruction for Lumbar Spine MRI at 3T: Clinical Feasibility

DEMO: Deep MR Parametric Mapping using Unsupervised Multi-tasking Framework

Development of Deep Learning based Cartilage Segmentation at 3D knee MRI for the use of Biomarker of Osteoarthritis

Differentiation of Benign and Malignant Vertebral Fractures on Spine MRI Using ResNet Deep Learning Compared to Radiologists' Reading

Feasibility of Femoral Cartilage Lesion Classification on Clinical MRIs using Deep Learning

Author:Mingrui Yang  Ceylan Colak  Mercan Aslan  Sibaji Gaj  Morgan Jones  Carl Winalski  Naveen Subhas  Xiaojuan Li  

Session Type:Digital Poster  

Session Date:Thursday, 20 May 2021  

Topic:Artificial Intelligence Applied to MSK MRI  

Session Name:Artificial Intelligence  

Program Number:4064  

Room Session:Concurrent 4  

Institution:Cleveland Clinic  

Fully automatic detection and voxel-wise mapping of vertebral body Modic changes using deep convolutional neural networks

Identification of Bone Marrow Lesions on Magnetic Resonance Imaging with Weakly Supervised Deep Learning

Less is more: zero-shot detection and transfer learning for facet arthropathy localization and classification on lumbar spine MRIs

MRI image synthesis with a conditional generative adversarial network using patch pooling

A pipeline combining deep learning and radiomics to automatically identify chronic lateral ankle instability from FS-PD MRI

Retrospective Contrast Tuning from a Single T1-weighted Image Using Deep Learning

Author:Yan Wu  Yajun Ma  Jiang Du  Lei Xing  

Session Type:Digital Poster  

Session Date:Thursday, 20 May 2021  

Topic:Artificial Intelligence Applied to MSK MRI  

Session Name:Artificial Intelligence  

Program Number:4063  

Room Session:Concurrent 4  

Institution:Stanford University  University of California San Diego  

Self-Supervised Deep Learning for Knee MRI Segmentation using Limited Labeled Training Datasets

Synovial Fluid Suppressed 3D T1? Mapping of Knee Cartilage using Deep Learning

Towards Clinical Translation of Fully Automatic Segmentation and 3D Biomarker Extraction of Lumbar Spine MRI