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Found 28 papers, 18 papers with code
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
In this work, we propose a data-centric framework, Biomedical Visual Instruction Tuning with Clinician Preference Alignment (BioMed-VITAL), that incorporates clinician preferences into both stages of generating and selecting instruction data for tuning biomedical multimodal foundation models.
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data.
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction.
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
Microstructures and Accuracy of Graph Recall by Large Language Models
Moreover, we find that more advanced LLMs have a striking dependence on the domain that a real-world graph comes from -- by yielding the best recall accuracy when the graph is narrated in a language style consistent with its original domain.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
R-Mixup: Riemannian Mixup for Biological Networks
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways.
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.
Neighborhood-Regularized Self-Training for Learning with Few Labels
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
Brain Network Transformer
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.
Effective and Interpretable fMRI Analysis via Functional Brain Network Generation
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks.
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are available.
Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure.
