21 citations · 32 across the 5 of their papers we have counts for
5 papers
Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation
Soroush Elyasi, Nasim Dadashi Serej, Julie Wall +1
Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of this shift are rarely disentangle…
EAGT: Echocardiography Augmentation for Generalisability and Transferability
Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej +1
Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently ann…
Automated Assessment of Transthoracic Echocardiogram Image Quality Using Deep Neural Networks
Robert B. Labs, Apostolos Vrettos, Jonathan Loo +1
Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. Th…
Echocardiographic Image Quality Assessment Using Deep Neural Networks
Robert B. Labs, Massoud Zolgharni, Jonathan P. Loo
Echocardiography image quality assessment is not a trivial issue in transthoracic examination. As the in vivo examination of heart structures gained prominence in cardiac diagnosis…
Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning
Neda Azarmehr, Xujiong Ye, Faraz Janan +3
Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmentin…