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Found 9 papers, 4 papers with code
Dilated deeply supervised networks for hippocampus segmentation in MRI
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone.
Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained.
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data.
Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis
AUROC was 75% for seropositive RA vs. PsA, 74% for seronegative RA vs. PsA and 67% for seropositive vs. seronegative RA.
Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically
Methods: We trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC.
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems
The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification.
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available.
