AIMC Topic: Algorithms

Clear Filters Showing 6441 to 6450 of 28713 articles

A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.

Medical principles and practice : international journal of the Kuwait University, Health Science Centre
OBJECTIVES: The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techni...

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening.

Big data
Survival models have found wider and wider applications in credit scoring recently due to their ability to estimate the dynamics of risk over time. In this research, we propose a Buckley-James safe sample screening support vector regression (BJS4VR) ...

Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs.

Neural networks : the official journal of the International Neural Network Society
While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency in...

HDConv: Heterogeneous kernel-based dilated convolutions.

Neural networks : the official journal of the International Neural Network Society
Dilated convolution has been widely used in various computer vision tasks due to its ability to expand the receptive field while maintaining the resolution of feature maps. However, the critical challenge is the gridding problem caused by the isomorp...

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR...

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

International journal of computer assisted radiology and surgery
PURPOSE: Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computin...

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters.

Science bulletin
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep lea...

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

Journal of applied clinical medical physics
PURPOSE: Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medica...

Improved quantitative parameter estimation for prostate T relaxometry using convolutional neural networks.

Magma (New York, N.Y.)
OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) f...

Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.

Sensors (Basel, Switzerland)
This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This...