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Found 5 papers, 1 papers with code
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.
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).
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.
