Artificial Intelligence Medical Compendium

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

Showing 64,121 to 64,130 of 231,309 articles

OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics

bioRxiv Jan 15, 2026
Deep visual proteomics (DVP) is an emerging approach for cell type-specific and spatially resolved proteomics. However, its broad adoption has been constrained by the lack of an open-source end-to-end workflow in a community-driven ecosystem. Here, w... read more 
systems biology

LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data

arXiv Jan 15, 2026
Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a res... read more 
Machine Learning cs.AI

V-Zero: Self-Improving Multimodal Reasoning with Zero Annotation

arXiv Jan 15, 2026
Recent advances in multimodal learning have significantly enhanced the reasoning capabilities of vision-language models (VLMs). However, state-of-the-art approaches rely heavily on large-scale human-annotated datasets, which are costly and time-consu... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG

Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text

arXiv Jan 15, 2026
Multimodal models excel in English, supported by abundant image-text and audio-text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely heavily on machine translation, whil... read more 
Machine Learning

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
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