Artificial Intelligence Medical Compendium

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

Showing 55,241 to 55,250 of 226,647 articles

Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases

arXiv Feb 3, 2026
Optical coherence tomography (OCT) has revolutionized retinal disease diagnosis with its high-resolution and three-dimensional imaging nature, yet its full diagnostic automation in clinical practices remains constrained by multi-stage workflows and c... read more 
Computer Vision and Pattern Recognition cs.AI

To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention

arXiv Feb 3, 2026
Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current agents suffer from critical inefficiency: they conduct excessive searches as... read more 
Information Retrieval

medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions

arXiv Feb 3, 2026
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: the challenge of defining signals that safely and effectively guide pol... read more 
Machine Learning

medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions

arXiv Feb 3, 2026
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: the challenge of defining signals that safely and effectively guide pol... read more 
Machine Learning

PQTNet: Pixel-wise Quantitative Thermography Neural Network for Estimating Defect Depth in Polylactic Acid Parts by Additive Manufacturing

arXiv Feb 3, 2026
Defect depth quantification in additively manufactured (AM) components remains a significant challenge for non-destructive testing (NDT). This study proposes a Pixel-wise Quantitative Thermography Neural Network (PQT-Net) to address this challenge fo... read more 
Computer Vision and Pattern Recognition

Invisible Clean-Label Backdoor Attacks for Generative Data Augmentation

arXiv Feb 3, 2026
With the rapid advancement of image generative models, generative data augmentation has become an effective way to enrich training images, especially when only small-scale datasets are available. At the same time, in practical applications, generativ... read more 
Computer Vision and Pattern Recognition

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)

arXiv Feb 3, 2026
Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from signal evolutions. However, the pattern-matching algorithms or neural ne... read more 
Machine Learning cs.AI cs.LG physics.med-ph

MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning

arXiv Feb 3, 2026
Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous agents, employing reinforcement learning with verifiable reward (RLVR)... read more 
Computer Vision and Pattern Recognition cs.AI

A Novel approach to portfolio construction

arXiv Feb 3, 2026
This paper proposes a machine learning-based framework for asset selection and portfolio construction, termed the Best-Path Algorithm Sparse Graphical Model (BPASGM). The method extends the Best-Path Algorithm (BPA) by mapping linear and non-linear d... read more 
Portfolio Management cs.LG q-fin.CP q-fin.RM stat.ML

Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability

arXiv Feb 3, 2026
We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By employing an InfoGAN-style objective, where we train a recognition model ... read more 
Computer Vision and Pattern Recognition
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