No description available
adi9-48
A deep learning model fine-tuned based on ResNet-50 for ECG image classification, assisting in cardiac disease detection
LANSG
GEM is a multimodal large language model that integrates ECG time series, 12-lead ECG images, and text to enable clinical evidence-based ECG interpretation.
Edoardo-BS
A self-supervised foundational model for broadly scalable cardiac applications, trained on 9.1 million 12-lead ECGs covering 164 cardiovascular diseases
A self-supervised pre-trained foundation model for ECG analysis, supporting detection of 164 cardiovascular diseases
PULSE-ECG
A multimodal large language model (MLLM) specifically designed for interpreting electrocardiogram (ECG) images, capable of handling various ECG-related tasks from diverse data sources.
deepsynthbody
A deep learning model for generating synthetic ECG that creates realistic electrocardiogram data for medical research and development