Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge

Y Song, S Ren, Y Lu, X Fu, KKL Wong - Computer Methods and Programs …, 2022 - Elsevier
Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …

Cardiac magnetic resonance imaging (CMRI) applications in patients with chest pain in the emergency department: a narrative review

H Zareiamand, A Darroudi, I Mohammadi, SV Moravvej… - Diagnostics, 2023 - mdpi.com
CMRI is the exclusive imaging technique capable of identifying myocardial edema,
endomyocardial fibrosis, pericarditis accompanied by pericardial effusions, and apical …

When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis

J Chen, MP Viana, SM Rafelski - Nature Methods, 2023 - nature.com
A key step toward biologically interpretable analysis of microscopy image-based assays is
rigorous quantitative validation with metrics appropriate for the particular application in use …

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

YR Wang, K Yang, Y Wen, P Wang, Y Hu, Y Lai… - Nature Medicine, 2024 - nature.com
Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function
assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However …

Cardiac MRI segmentation with sparse annotations: ensembling deep learning uncertainty and shape priors

F Guo, M Ng, G Kuling, G Wright - Medical Image Analysis, 2022 - Elsevier
The performance of deep learning for cardiac magnetic resonance imaging (MRI)
segmentation is oftentimes degraded when using small datasets and sparse annotations for …

Stochastic co-teaching for training neural networks with unknown levels of label noise

BD de Vos, GE Jansen, I Išgum - Scientific reports, 2023 - nature.com
Label noise hampers supervised training of neural networks. However, data without label
noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical …

A comparative study of state-of-the-art skin image segmentation techniques with CNN

G Nasreen, K Haneef, M Tamoor, A Irshad - Multimedia Tools and …, 2023 - Springer
Skin cancer is caused by genetic uncertainty or an irregular growth of cells, mostly grows
when our skin is exposed to sun. In people, melanoma is a common type of cancer …

[HTML][HTML] From accuracy to reliability and robustness in cardiac magnetic resonance image segmentation: a review

F Galati, S Ourselin, MA Zuluaga - Applied Sciences, 2022 - mdpi.com
Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR)
image segmentation has achieved state-of-the-art performance. Despite achieving inter …

Estimating uncertainty in neural networks for cardiac MRI segmentation: a benchmark study

M Ng, F Guo, L Biswas, SE Petersen… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated
cardiac magnetic resonance image segmentation. However, when using CNNs in a large …

ViT-FRD: A vision transformer model for cardiac MRI image segmentation based on feature recombination distillation

C Fan, Q Su, Z Xiao, H Su, A Hou, B Luan - IEEE Access, 2023 - ieeexplore.ieee.org
Cardiac magnetic resonance imaging analysis has been a useful tool in screening patients
for heart disease. Early, timely and accurate diagnosis of diseases of the heart series is the …