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Found 11 papers, 7 papers with code
A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing
Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life.
Heterogenous Ensemble of Models for Molecular Property Prediction
Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property prediction tasks.
Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition
Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis.
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains.
A Framework for Neural Topic Modeling of Text Corpora
Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields.
Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models
We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms.
COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations
We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic.
A Flexible and Intelligent Framework for Remote Health Monitoring Dashboards
Developing and maintaining monitoring panels is undoubtedly the main task in the remote patient monitoring (RPM) systems.
Group-Connected Multilayer Perceptron Networks
GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations.
TAPER: Time-Aware Patient EHR Representation
The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes.
ECG Heartbeat Classification: A Deep Transferable Representation
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system.
