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Found 12 papers, 3 papers with code
The object detection method aids in image reconstruction evaluation and clinical interpretation of meniscal abnormalities
This study investigates the relationship between deep learning (DL) image reconstruction quality and anomaly detection performance, and evaluates the efficacy of an artificial intelligence (AI) assistant in enhancing radiologists' interpretation of meniscal anomalies on reconstructed images.
Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls
The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0. 55T, both qualitatively and quantitatively, in terms of comparing segmentation performance, areas of improvement, and compartment-wise cartilage thickness values between 0. 55T vs. 3. 0T.
Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications
The swarm consensus votes outperformed individual and majority vote decisions in both the radiologists and resident cohorts.
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
These results are encouraging, and highlight the necessity for attention to this problem by the image reconstruction community, as a milestone for the introduction of DL reconstruction in clinical practice.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images
Results: The overall accuracy and weighted Cohen's kappa reported for ACL injury classification were higher using the 2D CNN (accuracy: 92% (233/254) and kappa: 0. 83) than the 3D CNN (accuracy: 89% (225/254) and kappa: 0. 83) (P = . 27).
Deep learning predicts total knee replacement from magnetic resonance images
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States.
Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust Training
Deep learning models have been shown to be successful in accelerating MRI reconstruction, over traditional methods.
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation
An experimental study was conducted showing the superior performance of the proposed method over a combination of a standard MRI reconstruction and segmentation method, as well as alternative deep learning based solutions.
Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning
Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.
Adversarial Policy Gradient for Deep Learning Image Augmentation
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 variance of the training dataset.
Distance Map Loss Penalty Term for Semantic Segmentation
We propose to use distance maps, derived from ground truth masks, to create a penalty term, guiding the network's focus towards hard-to-segment boundary regions.
