Back

Semi-supervised deep learning approach for left ventricle segmentation with limited annotations.

29 August 2025 (14:15 - 15:00)
Organised by: Logo
Congress Presentation Part of: Digital tools and wearables: transforming cardiovascular care Deep Learning ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

Photo
Ms Fatemah Aladwani

(Kuwait)

6 More presentations in this session

Presentation thumbnail
Unveiling the hidden local hemodynamics of peripheral circulation: integration of computational fluid dynamics and diastolic pressure decay
Miss Y. Alan (Istanbul, TR)
Abstract Slides Video
Presentation thumbnail
Implementation of an artificial intelligence-enabled stethoscope in primary care: utilisation and incentive characterisation in the TRICORDER programme
Doctor S. Nakhare (London, GB)
Abstract Slides Video
Presentation thumbnail
Adequately Beta-blocked? Insights from longitudinal wearable data in the All of Us Research Program
Doctor A. Nargesi (Boston, US)
Abstract Slides Video
Presentation thumbnail
Feasibility of utilizing machine learning to detect coronary artery disease via wearable devices
Mr R. Xu (Chengdu, CN)
Abstract Slides Video
Presentation thumbnail
Non-invasive estimation of hemodynamic parameters in pulmonary hypertension with a deep learning approach integrating all B-mode cine loops in an echocardiographic exam
Ms L. Cheng (Leiden, NL)
Abstract Slides Video

Access the full session

Session thumbnail
Digital tools and wearables: transforming cardiovascular care

The Event

Event poster

ESC Congress 2025

29 August 2025 14:15 CET
You may be interested in
thumbnail
Congress Presentation
CMR-Seg: a fully automated deep learning-based cardiac MRI segmentation and quantification
thumbnail
Congress Presentation
Deep learning enables the fully automated quantification of high-resolution cardiac CT images: human in loop for model development
thumbnail
Congress Presentation
Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources