Artificial Intelligence Medical Compendium

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

Showing 60,031 to 60,040 of 228,014 articles

PhaseMark: A Post-hoc, Optimization-Free Watermarking of AI-generated Images in the Latent Frequency Domain

arXiv Jan 19, 2026
The proliferation of hyper-realistic images from Latent Diffusion Models (LDMs) demands robust watermarking, yet existing post-hoc methods are prohibitively slow due to iterative optimization or inversion processes. We introduce PhaseMark, a single-s... read more 
Computer Vision and Pattern Recognition

A Streamlined Attention-Based Network for Descriptor Extraction

arXiv Jan 19, 2026
We introduce SANDesc, a Streamlined Attention-Based Network for Descriptor extraction that aims to improve on existing architectures for keypoint description. Our descriptor network learns to compute descriptors that improve matching without modify... read more 
Computer Vision and Pattern Recognition

Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroups

arXiv Jan 19, 2026
AI models for medical diagnosis often exhibit uneven performance across patient populations due to heterogeneity in disease prevalence, imaging appearance, and clinical risk profiles. Existing algorithmic fairness approaches typically seek to reduce ... read more 
Computer Vision and Pattern Recognition

Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures

arXiv Jan 19, 2026
Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides, degrading computer vision segmentation accuracy. Pixel-level annotation of low-light crac... read more 
Computer Vision and Pattern Recognition

GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure

arXiv Jan 19, 2026
This paper presents GridNet-HD, a multi-modal dataset for 3D semantic segmentation of overhead electrical infrastructures, pairing high-density LiDAR with high-resolution oblique imagery. The dataset comprises 7,694 images and 2.5 billion points anno... read more 
Computer Vision and Pattern Recognition

Think3D: Thinking with Space for Spatial Reasoning

arXiv Jan 19, 2026
Understanding and reasoning about the physical world requires spatial intelligence: the ability to interpret geometry, perspective, and spatial relations beyond 2D perception. While recent vision large models (VLMs) excel at visual understanding, the... read more 
Computer Vision and Pattern Recognition

Enhancing Generalization in Sickle Cell Disease Diagnosis through Ensemble Methods and Feature Importance Analysis

arXiv Jan 19, 2026
This work presents a novel approach for selecting the optimal ensemble-based classification method and features with a primarly focus on achieving generalization, based on the state-of-the-art, to provide diagnostic support for Sickle Cell Disease us... read more 
Machine Learning

Extracting useful information about reversible evolutionary processes from irreversible evolutionary accumulation models

arXiv Jan 19, 2026
Evolutionary accumulation models (EvAMs) are an emerging class of machine learning methods designed to infer the evolutionary pathways by which features are acquired. Applications include cancer evolution (accumulation of mutations), anti-microbial r... read more 
Populations and Evolution stat.ME

Early Prediction of Type 2 Diabetes Using Multimodal data and Tabular Transformers

arXiv Jan 19, 2026
This study introduces a novel approach for early Type 2 Diabetes Mellitus (T2DM) risk prediction using a tabular transformer (TabTrans) architecture to analyze longitudinal patient data. By processing patients` longitudinal health records and bone-re... read more 
Computer Vision and Pattern Recognition cs.LG

Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation

arXiv Jan 19, 2026
Self-supervised pretraining in remote sensing is mostly done using mid-spatial resolution (MR) image datasets due to their high availability. Given the release of high-resolution (HR) datasets, we ask how HR datasets can be included in self-supervise... read more 
Computer Vision and Pattern Recognition
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