Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
[HTML][HTML] Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …
biological processes and electrical activity of heart muscles. Machine learning and deep …
Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions
Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …
produced by the contraction and relaxation of the cardiac muscles. It has been established …
Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound
Elastography Ultrasound provides elasticity information of the tissues, which is crucial for
understanding the density and texture, allowing for the diagnosis of different medical …
understanding the density and texture, allowing for the diagnosis of different medical …
Geocrack: A high-resolution dataset for segmentation of fracture edges in geological outcrops
GeoCrack is the first large-scale open source annotated dataset of fracture traces from
geological outcrops, enabling deep learning-based fracture segmentation, setting a new …
geological outcrops, enabling deep learning-based fracture segmentation, setting a new …
Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US
image segmentation is crucial in image analysis. Recently, deep learning-based methods …
image segmentation is crucial in image analysis. Recently, deep learning-based methods …
CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments
In our research, we address the problem of coordination and planning in heterogeneous
multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this …
multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this …
Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings
A Karthik, K Aggarwal, A Kapoor, D Singh, L Hu… - BMC Medical …, 2024 - Springer
Background Conventional MR acceleration techniques, such as compressed sensing,
parallel imaging, and half Fourier often face limitations, including noise amplification …
parallel imaging, and half Fourier often face limitations, including noise amplification …
Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach
J Bin, M Wu, M Huang, Y Liao, Y Yang, X Shi… - BMC Medical Imaging, 2024 - Springer
Background To design a pulmonary ground-glass nodules (GGN) classification method
based on computed tomography (CT) radiomics and machine learning for prediction of …
based on computed tomography (CT) radiomics and machine learning for prediction of …
[HTML][HTML] Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images
P Prakasam - BMC Medical Imaging, 2024 - pmc.ncbi.nlm.nih.gov
Background Automatic diagnosis and brain hemorrhage segmentation in Computed
Tomography (CT) may be helpful in assisting the neurosurgeon in developing treatment …
Tomography (CT) may be helpful in assisting the neurosurgeon in developing treatment …