Welcome to the Deep Learning For Mobile Health (DL4mHealth) Research Lab. We are committed to advancing the field of mobile health through the application of cutting-edge deep learning techniques. Our mission is to develop innovative solutions that harness the power of mobile devices and deep learning to improve healthcare access, delivery, and outcomes.
Deep Learning for Mobile Health Lab
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Repositories
Showing 10 of 15 repositories
- cogent Public
CoGenT: A Unified Contrastive-Generative Framework for Time Series Classification. Published in IEEE Transactions on AI
DL4mHealth/cogent’s past year of commit activity - ERP-Benchmark Public
Benchmarking ERP Analysis with Manual Features, Deep Learning, and Foundation Models
DL4mHealth/ERP-Benchmark’s past year of commit activity - Medformer Public
[Neurips 2024] A Multi-Granularity Patching Transformer for Medical Time-Series Classification
DL4mHealth/Medformer’s past year of commit activity - MedTS_Evaluation Public
Evaluate Medical Time Series with Subject-Independent instead of Subject-Dependent
DL4mHealth/MedTS_Evaluation’s past year of commit activity - TS-Contrastive-Augmentation-Recommendation Public
Recommend effective augmentations for self-supervised contrastive learning tailored for your time series dataset
DL4mHealth/TS-Contrastive-Augmentation-Recommendation’s past year of commit activity - .github Public
DL4mHealth/.github’s past year of commit activity - Contrastive-Learning-in-Medical-Time-Series-Survey Public
A Systematic Review: Self-Supervised Contrastive Learning for Medical Time Series
DL4mHealth/Contrastive-Learning-in-Medical-Time-Series-Survey’s past year of commit activity
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