Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 61,101 to 61,110 of 228,300 articles

Enhancing Visual In-Context Learning by Multi-Faceted Fusion

arXiv Jan 15, 2026
Visual In-Context Learning (VICL) has emerged as a powerful paradigm, enabling models to perform novel visual tasks by learning from in-context examples. The dominant "retrieve-then-prompt" approach typically relies on selecting the single best visua... read more 
Computer Vision and Pattern Recognition

Beyond Single Prompts: Synergistic Fusion and Arrangement for VICL

arXiv Jan 15, 2026
Vision In-Context Learning (VICL) enables inpainting models to quickly adapt to new visual tasks from only a few prompts. However, existing methods suffer from two key issues: (1) selecting only the most similar prompt discards complementary cues fro... read more 
Computer Vision and Pattern Recognition

VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation

arXiv Jan 15, 2026
Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which i... read more 
Computer Vision and Pattern Recognition

LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning

arXiv Jan 15, 2026
Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critical Perception Gap in distillation: student models frequently mimic a te... read more 
Computer Vision and Pattern Recognition cs.AI

Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation

arXiv Jan 15, 2026
Causal discovery aims to recover ``what causes what'', but classical constraint-based methods (e.g., PC, FCI) suffer from error propagation, and recent LLM-based causal oracles often behave as opaque, confidence-free black boxes. This paper introduce... read more 
Machine Learning cs.AI stat.ML

MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging

arXiv Jan 15, 2026
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, i... read more 
Artificial Intelligence cs.CV cs.ET cs.LG cs.SE

RAG-3DSG: Enhancing 3D Scene Graphs with Re-Shot Guided Retrieval-Augmented Generation

arXiv Jan 15, 2026
Open-vocabulary 3D Scene Graph (3DSG) generation can enhance various downstream tasks in robotics, such as manipulation and navigation, by leveraging structured semantic representations. A 3DSG is constructed from multiple images of a scene, where ob... read more 
Computer Vision and Pattern Recognition cs.AI cs.RO

Bias in the Shadows: Explore Shortcuts in Encrypted Network Traffic Classification

arXiv Jan 15, 2026
Pre-trained models operating directly on raw bytes have achieved promising performance in encrypted network traffic classification (NTC), but often suffer from shortcut learning-relying on spurious correlations that fail to generalize to real-world d... read more 
Machine Learning cs.NI

From Physical Degradation Models to Task-Aware All-in-One Image Restoration

arXiv Jan 15, 2026
All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning module... read more 
Computer Vision and Pattern Recognition

ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and TEst-time Generative Adaptation

arXiv Jan 15, 2026
We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation. Prior works rely either on a 3D data prior or a 2D generative prior to compensate for missing visua... read more 
Computer Vision and Pattern Recognition
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