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Found 9 papers, 5 papers with code
Towards trustworthy seizure onset detection using workflow notes
We find that our multilabel model significantly improves overall seizure onset detection performance (+5. 9 AUROC points) while greatly improving performance among subgroups (up to +8. 3 AUROC points), and decreases false positives on non-epileptiform abnormalities by 8 FPR points.
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography.
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Optimization plays a key role in the training of deep neural networks.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset
In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset.
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
Neuromorphic image sensors produce activity-driven spiking output at every pixel.
Spatiotemporal Feature Learning for Event-Based Vision
Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution.
