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Found 12 papers, 1 papers with code
A comparative study between vision transformers and CNNs in digital pathology
Due to the sparse availability of annotated whole slide images, we further compared both models pretrained on large amounts of unlabeled whole-slide images using state-of-the-art self-supervised approaches.
Deep autofocus with cone-beam CT consistency constraint
To adapt the backprojection operation accordingly, a motion estimation strategy is necessary.
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.
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
In medical imaging, this lack of comprehensibility of the results is a sensitive issue.
Image Quality Assessment for Rigid Motion Compensation
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures.
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.
PYRO-NN: Python Reconstruction Operators in Neural Networks
The high level Python API allows a simple use of the layers as known from Tensorflow.
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.
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.
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.
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.
