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Found 21 papers, 10 papers with code
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios.
GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs
Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch.
A Foundational Generative Model for Breast Ultrasound Image Analysis
Additionally, we characterized the scaling effect of using generated data which was as effective as the collected real-world data for training diagnostic models.
LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive Classifier
To address this issue, we first propose a general baseline framework to incorporate image-level supervision into the training process of a pixel-level segmentation model, making the trained network perform semantic segmentation on newly introduced categories in the classification data.
Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric
This short paper presents preliminary research on the Case-Enhanced Vision Transformer (CEViT), a similarity measurement method aimed at improving the explainability of similarity assessments for image data.
Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33. 5% in specificity with the same sensitivity, improving their performance by providing predictions with an interpretable decision-making process.
When Box Meets Graph Neural Network in Tag-aware Recommendation
Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags.
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Thus, the study aims to develop a high-precision choroidal vessel segmentation model with limited labor using HITL framework.
Knowledge Graph Pruning for Recommendation
Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement.
DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs.
EyeGPT: Ophthalmic Assistant with Large Language Models
Artificial intelligence (AI) has gained significant attention in healthcare consultation due to its potential to improve clinical workflow and enhance medical communication.
Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views
As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier
Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases.
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection
To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection
In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important.
PointScatter: Point Set Representation for Tubular Structure Extraction
Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features
Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences.
FPGA-based Binocular Image Feature Extraction and Matching System
Image feature extraction and matching is a fundamental but computation intensive task in machine vision.
