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Found 11 papers, 1 papers with code
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Consistent performance was observed across the three independent multicenter test sets with Cdt values of 0. 707 (UPenn-GBM, internal test set), 0. 672 (UCSF-PDGM, first external test set) and 0. 618 (RHUH-GBM, second external test set).
Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning
We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment.
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
With iterative continual learning (i. e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0. 914, which is identical to the sensitivity using mixed data for training.
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels.
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.
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.
Temporal and volumetric denoising via quantile sparse image prior
For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data.
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.
