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Found 6 papers, 2 papers with code
A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography
This is the first optimization of a 3D inverse model in an image reconstruction context in OCT.
Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases.
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology.
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
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.
PYRO-NN: Python Reconstruction Operators in Neural Networks
The high level Python API allows a simple use of the layers as known from Tensorflow.
