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Found 19 papers, 6 papers with code
A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces
Since thermal imagery offers a unique modality to investigate pain, the U. S. National Institutes of Health (NIH) has collected a large and diverse set of cancer patient facial thermograms for AI-based pain research.
Novel Categories Discovery Via Constraints on Empirical Prediction Statistics
As an alternative to the traditional pseudo-labeling-based approaches, we leverage the connection between the data sampling and the provided multinoulli (categorical) distribution of novel classes.
Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial Pairs
For a variety of biometric cross-spectral tasks, Visible-Thermal (VT) facial pairs are used.
NEV-NCD: Negative Learning, Entropy, and Variance regularization based novel action categories discovery
During our experiments with UCF101 and multi-view action dataset, NEV-NCD achieves ~ 83% classification accuracy in test instances of labeled data.
When Visible-to-Thermal Facial GAN Beats Conditional Diffusion
Thermal facial imagery offers valuable insight into physiological states such as inflammation and stress by detecting emitted radiation in the infrared spectrum, which is unseen in the visible spectra.
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis
To address these limitations, we propose a new class of pseudo-value-based deep learning models for multi-state survival analysis, where we show that pseudo values - designed to handle censoring - can be a natural replacement for estimating the multi-state model quantities when derived from a consistent estimator.
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis
To overcome the challenges of existing federated survival analysis methods, we leverage the predictive accuracy of the deep learning models and the power of pseudo values to propose a first-of-its-kind, pseudo value-based deep learning model for federated survival analysis (FSA) called FedPseudo.
Intelligent Sight and Sound: A Chronic Cancer Pain Dataset
Cancer patients experience high rates of chronic pain throughout the treatment process.
Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels
These combined data are captured from similar sensors in order to bootstrap the training and transfer learning task, especially valuable because visible-thermal face datasets are limited.
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data
But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition.
Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters.
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
Neural networks are known to model statistical interactions, but they entangle the interactions at intermediate hidden layers for shared representation learning.
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
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Measuring and Predicting Tag Importance for Image Retrieval
Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems.
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research.
