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Found 16 papers, 5 papers with code
Torchmeta: A Meta-Learning library for PyTorch
The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Computed tomography is widely used to examine internal structures in a non-destructive manner.
The TCGA Meta-Dataset Clinical Benchmark
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string.
Projection image-to-image translation in hybrid X-ray/MR imaging
Our approach is capable of creating X-ray projection images with natural appearance.
MR to X-Ray Projection Image Synthesis
The perceptual-loss showed to be able to preserve most of the high-frequency details in the projection images and, thus, is recommended for the underlying task and similar problems.
Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions
Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.
Precision Learning: Towards Use of Known Operators in Neural Networks
We demonstrate that use of known transforms is able to change maximal error bounds.
Frangi-Net: A Neural Network Approach to Vessel Segmentation
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter.
Precision Learning: Reconstruction Filter Kernel Discretization
In this paper, we present substantial evidence that a deep neural network will intrinsically learn the appropriate way to discretize the ideal continuous reconstruction filter.
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
The results demonstrate that the proposed method is superior to ray-by-ray interpolation and is able to deliver sharper images using the same amount of parallel-beam input projections which is crucial for interventional applications.
Learning with Known Operators reduces Maximum Training Error Bounds
We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging.
Reconstruction of Voxels with Position- and Angle-Dependent Weightings
In this work we first formulate this reconstruction problem in terms of a system matrix and weighting part.
Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom
The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e. g., to biology and cultural heritage research.
