Essays about: "Cardiovascular Models"

Showing result 1 - 5 of 45 essays containing the words Cardiovascular Models.

  1. 1. Self-supervised representation learning from electrocardiogram data for medical applications

    University essay from Lunds universitet/Matematik LTH

    Author : Matilda Andersson; [2023]
    Keywords : Self-supervised learning; Deep learning; Cardiovascular disease; Electrocardiogram; ECG; SimCLR; BYOL; VICReg; Mathematics and Statistics;

    Abstract : Cardiovascular diseases are the leading cause of death worldwide, increasing yearly. However, many abnormalities in heart cycles can be discovered and treated years before the onset of diseases. But in most societies, regular health checkups are a concept reserved for cars, not humans. READ MORE

  3. 2. Bootstrapping methods for assessing causality in survival analysis: A case study on major adverse cardiovascular events

    University essay from Lunds universitet/Matematisk statistik

    Author : Paulina Benthem Ciano; [2023]
    Keywords : Causality; Graphical models; Bootstrap; Survival analysis; Additive hazards model.; Mathematics and Statistics;

    Abstract : The combination of graphical models with Aalen's additive hazards model, resulting in a model known as dynamical path analysis, permits assessing the effects of different variables on times until an event and decomposing these total effects into direct and indirect effects. This thesis proposes novel bootstrapping methods designed for left-truncated right-censored data, conditional on covariates within the framework of Aalen's additive hazards model, in order to obtain confidence intervals for the estimates. READ MORE

  4. 3. Explainable Machine Learning in Cardiovascular Diagnostics

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Alexander Gutell; Ludvig Skare; [2023]
    Keywords : ;

    Abstract : The major challenges in implementing machine learning models in medical applications stemfrom ethical and accountability concerns, which arise from the lack of insight and understandingof the models' inner workings and reasoning. This opaqueness has resulted in the emergenceof a new subfield of machine learning called Explainability, which aims to develop and deploymethods to gain insight into how input data is weighted and propagated through a machinelearning algorithm. READ MORE

  5. 4. Reconstruction of Accelerated Cardiovascular MRI data

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Hussnain Khalid; [2023]
    Keywords : medical imaging; deep learning; CNN; Magnetic resonance imaging; MRI; Cardiac MRI; Cardiac; Cardiovascular; reconstruction; 4D flow MRI; Parallel Imaging; Compressed Sensing; FlowVN; Flow Variational Network; K-space; Reference images; sensitivity maps; Respiratory motion; undersampled images;

    Abstract : Magnetic resonance imaging (MRI), is a noninvasive medical imaging testing techniquewhich is used to produce detailed images of internal structure of the human body, includingbones, muscles, organs, and blood vessels. MRI scanners use large magnets and radiowaves to create images of the body. READ MORE

  6. 5. Generalized super-resolution of 4D Flow MRI : extending capabilities using ensemble learning

    University essay from Linköpings universitet/Institutionen för medicinsk teknik

    Author : Adam Hjalmarsson; Leon Ericsson; [2023]
    Keywords : 4D Flow MRI; Ensemble learning; Super-resolution; Machine learning; Neural networks; 4DFlowNet;

    Abstract : 4D Flow Magnet Resonance Imaging (4D Flow MRI) is a novel non-invasive technique for imaging of cardiovascular blood flow. However, when utilized as a stand-alone analysis method, 4D Flow MRI has certain limitations including limited spatial resolution and noise artefacts, motivating the application of dedicated post-processing tools. READ MORE