Artificial Intelligence Medical Compendium

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

Showing 31,421 to 31,430 of 220,544 articles

Machine-learned many-body potentials for charged colloids reveal gas-liquid spinodal instabilities only in the strong-coupling regime of primitive models.

The Journal of chemical physics Apr 14, 2026
Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like-charge attraction. Evidence for this phenomenon was also observed in primitive-model ... read more 

SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models

arXiv Apr 14, 2026
The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL) with reward models. A fundamental gap separates them. SFT optimizes the denoiser only on ground-tru... read more 
Machine Learning cs.AI

Efficient Semantic Image Communication for Traffic Monitoring at the Edge

arXiv Apr 14, 2026
Many visual monitoring systems operate under strict communication constraints, where transmitting full-resolution images is impractical and often unnecessary. In such settings, visual data is often used for object presence, spatial relationships, and... read more 
Computer Vision and Pattern Recognition cs.AI cs.NI

PromptEcho: Annotation-Free Reward from Vision-Language Models for Text-to-Image Reinforcement Learning

arXiv Apr 14, 2026
Reinforcement learning (RL) can improve the prompt following capability of text-to-image (T2I) models, yet obtaining high-quality reward signals remains challenging: CLIP Score is too coarse-grained, while VLM-based reward models (e.g., RewardDance) ... read more 
Computer Vision and Pattern Recognition cs.AI

OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner

arXiv Apr 14, 2026
The Diffusion Probabilistic Model (DPM) achieves remarkable performance in image generation, while its increasing parameter size and computational overhead hinder its deployment in practical applications. To improve this, the existing literature focu... read more 
Computer Vision and Pattern Recognition

Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI

arXiv Apr 14, 2026
Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that are semantically incoherent (e.g., classifying a malignant tumor as be... read more 
Computer Vision and Pattern Recognition

Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging

arXiv Apr 14, 2026
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapt... read more 
Machine Learning cs.AI cs.CV

Transformer Based Machine Fault Detection From Audio Input

arXiv Apr 14, 2026
In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get real time data on machine behavior from the field. Traditionally, Convolutional Neural Net (CNN) archi... read more 
Sound cs.LG

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

arXiv Apr 14, 2026
While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism agai... read more 
Cryptography and Security cs.LG

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

arXiv Apr 14, 2026
While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism agai... read more 
Cryptography and Security cs.LG
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