Artificial Intelligence Medical Compendium

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

Showing 51,411 to 51,420 of 225,182 articles

Text-to-Vector Conversion for Residential Plan Design

arXiv Feb 11, 2026
Computer graphics, comprising both raster and vector components, is a fundamental part of modern science, industry, and digital communication. While raster graphics offer ease of use, its pixel-based structure limits scalability. Vector graphics, def... read more 
Computer Vision and Pattern Recognition

Collaborative Threshold Watermarking

arXiv Feb 11, 2026
In federated learning (FL), $K$ clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a h... read more 
Machine Learning

Kill it with FIRE: On Leveraging Latent Space Directions for Runtime Backdoor Mitigation in Deep Neural Networks

arXiv Feb 11, 2026
Machine learning models are increasingly present in our everyday lives; as a result, they become targets of adversarial attackers seeking to manipulate the systems we interact with. A well-known vulnerability is a backdoor introduced into a neural ne... read more 
Machine Learning cs.AI cs.CR cs.CV

RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation

arXiv Feb 11, 2026
Multimodal large language models (MLLMs) are increasingly adopted in remote sensing (RS) and have shown strong performance on tasks such as RS visual grounding (RSVG), RS visual question answering (RSVQA), and multimodal dialogue. However, hallucinat... read more 
Computer Vision and Pattern Recognition cs.AI

DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples

arXiv Feb 11, 2026
Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods rely on category-specific training, which limits their flexibility in... read more 
Computer Vision and Pattern Recognition

DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories

arXiv Feb 11, 2026
Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dependencies inherent in realistic visual streams, where information is d... read more 
Computer Vision and Pattern Recognition cs.IR

Hyperspectral Smoke Segmentation via Mixture of Prototypes

arXiv Feb 11, 2026
Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-tra... read more 
Computer Vision and Pattern Recognition

Stride-Net: Fairness-Aware Disentangled Representation Learning for Chest X-Ray Diagnosis

arXiv Feb 11, 2026
Deep neural networks for chest X-ray classification achieve strong average performance, yet often underperform for specific demographic subgroups, raising critical concerns about clinical safety and equity. Existing debiasing methods frequently yield... read more 
Computer Vision and Pattern Recognition

Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation

arXiv Feb 11, 2026
Vision-Language Models (VLMs) have shown promise in generating plotting code from chart images, yet achieving structural fidelity remains challenging. Existing approaches largely rely on supervised fine-tuning, encouraging surface-level token imitati... read more 
Computer Vision and Pattern Recognition

CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control

arXiv Feb 11, 2026
Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multiple pre-trained models remains an open challenge. Current approaches largely treat composition as an a... read more 
Machine Learning
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