ecgxai
importedsoftware/ecgxai
Neatly packaged AI methods for explainable ECG analysis
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- AGPL-3.0(osi)
- Status
- active
- Maturity
- deployed
- Organization
- UMCUtrecht-ECGxAI
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/UMCUtrecht-ECGxAI/ecgxai
- Documentation
- unknown
- Tags
- deep-learning · deep-neural-networks · ecg · explainable-ai · variational-autoencoder
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- IMLE-Netecg · explainable-ai
Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"
- XAI_SignalCAMecg · explainable-ai
PyTorch implementation of the lightweight CNN-based ECG classification approach with integrated explainable AI (XAI) capabilities presented in the paper "Lightweight Data-driven ECG Classification…
- 3d_very_deep_vaevariational-autoencoder
PyTorch implementations of variational autoencoders for 3D images
- paccmann_omicsvariational-autoencoder
Generative models for transcriptomics profiles and proteins
- PILOT-GM-VAEvariational-autoencoder
Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders
- sisuavariational-autoencoder
SemI-SUpervised generative Autoencoder models for single cell data
- api.github.com/repos/UMCUtrecht-ECGxAI/ecgxairetrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-07-02, 101 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/4.json→ .entries["ecgxai"]
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