most citedA Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN

35 citations · 67 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV20244 cited

Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients

Müjde Akdeniz, Claudia Alessandra Manetti, Tijmen Koopsen +6

How well the heart is functioning can be quantified through measurements of myocardial deformation via echocardiography. Clinical assessment of cardiac function is generally focuse…

eess.IV20245 cited

Cardiac valve event timing in echocardiography using deep learning and triplane recordings

Benjamin Strandli Fermann, John Nyberg, Espen W. Remme +9

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of e…

eess.IV202435 cited

A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN

Cristiana Tiago, Andrew Gilbert, Ahmed S. Beela +3

Due to privacy issues and limited amount of publicly available labeled datasets in the domain of medical imaging, we propose an image generation pipeline to synthesize 3D echocardi…

eess.IV202423 cited

A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images

Cristiana Tiago, Sten Roar Snare, Jurica Sprem +1

Currently, medical image domain translation operations show a high demand from researchers and clinicians. Amongst other capabilities, this task allows the generation of new medica…