Search Results for author:
Found 5 papers, 3 papers with code
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences.
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.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses.
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.
Assessing Robustness to Noise: Low-Cost Head CT Triage
We then show that the trained model is robust to reduced tube current and fewer projections, with the AUROC dropping only 0. 65% for images acquired with a 16x reduction in tube current and 0. 22% for images acquired with 8x fewer projections.
