Artificial Intelligence Medical Compendium

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

Showing 18,951 to 18,960 of 214,800 articles

UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation

arXiv May 14, 2026
RGB-T semantic segmentation requires strictly aligned VIS-IR-Label triplets; however, such aligned triplet data are often scarce in real-world scenarios. Existing generative augmentation methods usually adopt cascaded generation paradigms, decomposin... read more 
Computer Vision and Pattern Recognition

MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models

arXiv May 14, 2026
This paper introduces a multi-label visual emotion analysis benchmark dataset for comprehensively evaluating the ability of multimodal large language models (MLLMs) to predict the emotions evoked by images. Recent user studies report an unintuitive f... read more 
Computer Vision and Pattern Recognition cs.AI

TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection

arXiv May 14, 2026
Urban vegetation monitoring plays a vital role in understanding environmental changes, yet comprehensive datasets for this purpose remain limited. To address this gap, we present the Temporal Remote-sensing Repository for Analyzing Change Detection (... read more 
Computer Vision and Pattern Recognition

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging

arXiv May 14, 2026
Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or masked reconstruction. They often do not adequately capitalize on a key... read more 
Computer Vision and Pattern Recognition

MiVE: Multiscale Vision-language features for reference-guided video Editing

arXiv May 14, 2026
Reference-guided video editing takes a source video, a text instruction, and a reference image as inputs, requiring the model to faithfully apply the instructed edits while preserving original motion and unedited content. Existing methods fall into t... read more 
Computer Vision and Pattern Recognition

EponaV2: Driving World Model with Comprehensive Future Reasoning

arXiv May 14, 2026
Data scaling plays a pivotal role in the pursuit of general intelligence. However, the prevailing perception-planning paradigm in autonomous driving relies heavily on expensive manual annotations to supervise trajectory planning, which severely limit... read more 
Computer Vision and Pattern Recognition

NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces

arXiv May 14, 2026
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field.... read more 
Machine Learning cs.AI

Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasoners

arXiv May 14, 2026
Recent unified models integrate multimodal understanding and generation within a single framework. However, an "understanding-generation gap" persists, where models can capture user intent but often fail to translate this semantic knowledge into prec... read more 
Computer Vision and Pattern Recognition

Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke

arXiv May 14, 2026
Deep learning and multi-modal fusion have demonstrated transformative potential in medical diagnosis by integrating diverse data sources. However, accurate prognosis for ischemic stroke remains challenging due to limitations in existing multi-modal a... read more 
Computer Vision and Pattern Recognition cs.AI

Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning

arXiv May 14, 2026
Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology remains challenging. We present a unified Deep Learning (DL) framewor... read more 
Computer Vision and Pattern Recognition cs.AI
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