24 citations · 57 across the 12 of their papers we have counts for
12 papers
Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement
Gabrielle Hoyer, Kenneth T Gao, Felix G Gassert +4
This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA…
Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee +2
Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative b…
The object detection method aids in image reconstruction evaluation and clinical interpretation of meniscal abnormalities
Natalia Konovalova, Aniket Tolpadi, Felix Liu +8
This study investigates the relationship between deep learning (DL) image reconstruction quality and anomaly detection performance, and evaluates the efficacy of an artificial inte…
Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications
Rutwik Shah, Bruno Astuto, Tyler Gleason +10
Radiologists today play a key role in making diagnostic decisions and labeling images for training A.I. algorithms. Low inter-reader reliability (IRR) can be seen between experts w…
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
Francesco Calivá, Kaiyang Cheng, Rutwik Shah +1
Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee tha…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images
Nikan K. Namiri, Io Flament, Bruno Astuto +6
Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. Materials and Methods: Th…
Deep learning predicts total knee replacement from magnetic resonance images
Aniket A. Tolpadi, Jinhee J. Lee, Valentina Pedoia +1
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slo…
Development of Conditional Random Field Insert for UNet-based Zonal Prostate Segmentation on T2-Weighted MRI
Peng Cao, Susan M. Noworolski, Olga Starobinets +6
Purpose: A conventional 2D UNet convolutional neural network (CNN) architecture may result in ill-defined boundaries in segmentation output. Several studies imposed stronger constr…
Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning
Justin D Krogue, Kaiyang V Cheng, Kevin M Hwang +13
Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reduc…
Adversarial Policy Gradient for Deep Learning Image Augmentation
Kaiyang Cheng, Claudia Iriondo, Francesco Calivá +3
The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and varian…
Distance Map Loss Penalty Term for Semantic Segmentation
Francesco Caliva, Claudia Iriondo, Alejandro Morales Martinez +2
Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes…