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Found 9 papers, 6 papers with code
SMPL-A: Modeling Person-Specific Deformable Anatomy
In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e. g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment.
End-to-end Ultrasound Frame to Volume Registration
Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield.
Cross-modal Attention for MRI and Ultrasound Volume Registration
In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.
Transducer Adaptive Ultrasound Volume Reconstruction
However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings.
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.
Knowledge-based Analysis for Mortality Prediction from CT Images
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
On a Sparse Shortcut Topology of Artificial Neural Networks
In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.
Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images
In this paper, we propose a deep learning based method, which takes both chest LDCT image patches and coronary artery calcification risk scores as input, for direct prediction of mortality risk of lung cancer subjects.
