Search Results for author:
Found 7 papers, 4 papers with code
Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series: A UK Biobank Study
However, these methods fail to early predict an individual's probability of COPD in the future based on subtle features in the spirogram.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
Denoising-Diffusion Alignment for Continuous Sign Language Recognition
The DDA further proposes the Denoising-Diffusion Autoencoder, which adds a decoder in the auxiliary condition diffusion to denoise the partial noisy bimodal representations via the designed DDA loss in self-supervised.
Ranked #3 on
Sign Language Recognition
on CSL-Daily
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
Sepsis is a leading cause of death in the ICU.
Local Patch Network with Global Attention for Infrared Small Target Detection
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP.
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification.
Ranked #1 on
Time Series Classification
on Physionet 2017 Atrial Fibrillation
(F1 (Hidden Test Set) metric)
