Search Results for author:
Found 47 papers, 13 papers with code
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
Examining Scientific Writing Styles from the Perspective of Linguistic Complexity
The observations suggest marginal differences between groups in syntactical and lexical complexity.
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
Analyzing Linguistic Complexity and Scientific Impact
In this context, scientific writing increasingly plays an important role in scholars' scientific careers.
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task.
Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements
COVID-19 resulted in an infodemic, which could erode public trust, impede virus containment, and outlive the pandemic itself.
Social and Information Networks Computers and Society
Coronavirus Knowledge Graph: A Case Study
The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months.
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure
Here we propose using the latest graph representation learning and embedding models to refine and complete biomedical knowledge graphs.
Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distance
This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance.
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality.
Spending Your Winning Lottery Better After Drawing It
In this paper, we demonstrate that it is unnecessary for spare retraining to strictly inherit those properties from the dense network.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era.
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events.
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop
The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.
Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power
In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics.
CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays
During training, the image branch leverages its learned attention to estimate pathology localization, which is then utilized to extract radiomic features from images in the radiomics branch.
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata
In this paper, we proposed a novel and simple data augmentation method based on patient metadata and supervised knowledge to create clinically accurate positive and negative augmentations for chest X-rays.
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification
Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements.
Prior Knowledge Enhances Radiology Report Generation
In this work, we propose to mine and represent the associations among medical findings in an informative knowledge graph and incorporate this prior knowledge with radiology report generation to help improve the quality of generated reports.
The Gene of Scientific Success
Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success.
Radiology Text Analysis System (RadText): Architecture and Evaluation
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.
Training Your Sparse Neural Network Better with Any Mask
Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable due to the reduced space and time complexity.
Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays
Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers.
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
In this paper, firstly, we provide a new perspective of gradient flow to understand the substandard performance of deep GCNs and hypothesize that by facilitating healthy gradient flow, we can significantly improve their trainability, as well as achieve state-of-the-art (SOTA) level performance from vanilla-GCNs.
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM).
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances
However, DNNs are notoriously fragile to the class imbalance in image classification.
Vision HGNN: An Image is More than a Graph of Nodes
In this paper, we enhance ViG by transcending conventional "pairwise" linkages and harnessing the power of the hypergraph to encapsulate image information.
Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality
This paper demonstrates the importance of XAI methods in cross-validation of feature attributions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value
This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database.
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data.
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses
To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans.
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
Motivated by the recent observations of model soups, which suggest that fine-tuned weights of multiple models can be merged to a better minima, we propose Instant Soup Pruning (ISP) to generate lottery ticket quality subnetworks, using a fraction of the original IMP cost by replacing the expensive intermediate pruning stages of IMP with computationally efficient weak mask generation and aggregation routine.
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
By dividing giant graph data, we build multiple independently and parallelly trained weaker GNNs (soup ingredient) without any intermediate communication, and combine their strength using a greedy interpolation soup procedure to achieve state-of-the-art performance.
An empirical study of using radiology reports and images to improve ICU mortality prediction
Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality.
Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers
Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China.
tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction
In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images.
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning
Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
Thought Graph: Generating Thought Process for Biological Reasoning
We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
Data accuracy is essential for scientific research and policy development.
EchoGen: Generating Conclusions from Echocardiogram Notes
Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.
