78 citations · 185 across the 9 of their papers we have counts for
9 papers
Towards trustworthy seizure onset detection using workflow notes
Khaled Saab, Siyi Tang, Mohamed Taha +3
A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models ma…
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu +4
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in mul…
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Siyi Tang, Amara Tariq, Jared Dunnmon +5
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…
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Florian Dubost, Erin Hong, Max Pike +5
Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inferen…
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Siyi Tang, Jared A. Dunnmon, Khaled Saab +5
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain un…
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
Florian Dubost, Erin Hong, Nandita Bhaskhar +3
Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised m…
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita +4
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from…
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
Rohan Ghosh, Siyi Tang, Mahdi Rasouli +2
Neuromorphic image sensors produce activity-driven spiking output at every pixel. These low-power consuming imagers which encode visual change information in the form of spikes hel…
Spatiotemporal Feature Learning for Event-Based Vision
Rohan Ghosh, Anupam Gupta, Siyi Tang +2
Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution. By only encoding changes in pixel intensity, th…