Artificial Intelligence Medical Compendium

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

Showing 47,691 to 47,700 of 224,199 articles

PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information

arXiv Feb 23, 2026
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence models can be costly and data-hungry. We propose PaReGTA, an LLM-based encoding framework that (i) conv... read more 
Machine Learning

Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation

arXiv Feb 23, 2026
Longitudinal medical report generation is clinically important yet remains challenging due to strict privacy constraints and the evolving nature of disease progression. Although federated learning (FL) enables collaborative training without data shar... read more 
Computer Vision and Pattern Recognition cs.LG

TeHOR: Text-Guided 3D Human and Object Reconstruction with Textures

arXiv Feb 23, 2026
Joint reconstruction of 3D human and object from a single image is an active research area, with pivotal applications in robotics and digital content creation. Despite recent advances, existing approaches suffer from two fundamental limitations. Firs... read more 
Computer Vision and Pattern Recognition cs.AI

HDR Reconstruction Boosting with Training-Free and Exposure-Consistent Diffusion

arXiv Feb 23, 2026
Single LDR to HDR reconstruction remains challenging for over-exposed regions where traditional methods often fail due to complete information loss. We present a training-free approach that enhances existing indirect and direct HDR reconstruction met... read more 
Computer Vision and Pattern Recognition

ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets

arXiv Feb 23, 2026
Beyond general recognition tasks, specialized domains including privacy-constrained medical applications and fine-grained settings often encounter data scarcity, especially for tail classes. To obtain less biased and more reliable models under such s... read more 
Computer Vision and Pattern Recognition

Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasets

arXiv Feb 23, 2026
Synthesizing missing modalities in multi-modal magnetic resonance imaging (MRI) is vital for ensuring diagnostic completeness, particularly when full acquisitions are infeasible due to time constraints, motion artifacts, and patient tolerance. Recent... read more 
Computer Vision and Pattern Recognition

InfScene-SR: Spatially Continuous Inference for Arbitrary-Size Image Super-Resolution

arXiv Feb 23, 2026
Image Super-Resolution (SR) aims to recover high-resolution (HR) details from low-resolution (LR) inputs, a task where Denoising Diffusion Probabilistic Models (DDPMs) have recently shown superior performance compared to Generative Adversarial Networ... read more 
Computer Vision and Pattern Recognition

Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis

arXiv Feb 23, 2026
Recent advances in multimodal learning have achieved remarkable success across diverse vision-language tasks. However, such progress heavily relies on large-scale image-text datasets, making training costly and inefficient. Prior efforts in dataset f... read more 
Computer Vision and Pattern Recognition

Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes

arXiv Feb 23, 2026
Dynamic predictions for longitudinal and time-to-event outcomes have become a versatile tool in precision medicine. Our work is motivated by the application of dynamic predictions in the decision-making process for primary biliary cholangitis patient... read more 
Machine Learning cs.LG stat.AP

Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications

arXiv Feb 23, 2026
Autonomous drone-based tree pruning needs accurate, real-time depth estimation from stereo cameras. Depth is computed from disparity maps using $Z = f B/d$, so even small disparity errors cause noticeable depth mistakes at working distances. Building... read more 
Computer Vision and Pattern Recognition eess.IV
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