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Found 7 papers, 2 papers with code
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty
Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and hand Pose tracking system, which provides uncertainty estimates of the computed pose, e. g., when a finger is hidden or its estimate is inconsistent with the observations in the input, to guide clinical decision-making.
Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis.
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems.
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.
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
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.
