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Found 4 papers, 1 papers with code
Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis
In this study, we address this challenge by prototyping a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging.
VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray
Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0. 90, vs. a mean Dice of 0. 73 for Mask R-CNN and 0. 72 for U-Net.
VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations.
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy.
