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Found 12 papers, 3 papers with code
The Calibration Gap between Model and Human Confidence in Large Language Models
Recent work has focused on the quality of internal LLM confidence assessments, but the question remains of how well LLMs can communicate this internal model confidence to human users.
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction.
Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships.
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts
Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications.
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation
Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos.
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery
To date, there are no effective treatments for most neurodegenerative diseases.
Physics-Guided Deep Neural Networks for Power Flow Analysis
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
