Artificial Intelligence Medical Compendium

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

Showing 30,281 to 30,290 of 220,100 articles

Frequency-Aware Flow Matching for High-Quality Image Generation

arXiv Apr 16, 2026
Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively adds Gaussian noise. However, because noise is injected in the latent domain, its impact on differ... read more 
Computer Vision and Pattern Recognition

UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentation

arXiv Apr 16, 2026
Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Post-irradiation materialography helps understand processes that impact fuel performance, such as co... read more 
Computer Vision and Pattern Recognition cs.LG

CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification

arXiv Apr 16, 2026
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single institution, failing to capture the pre... read more 
Computer Vision and Pattern Recognition

"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations

arXiv Apr 16, 2026
The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However, the reactive nature of LLMs, which respond only when prompted, limits their effectiveness in collab... read more 
Computation and Language cs.AI cs.HC cs.LG

Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images

arXiv Apr 16, 2026
Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random masking methods make SSL less effective for medical images due to the... read more 
Computer Vision and Pattern Recognition

H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

arXiv Apr 16, 2026
As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a frequently-faced issue. To solve it, the few-shot anomaly detection (FSAD) scheme is attracting i... read more 
Computer Vision and Pattern Recognition cs.LG

Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs

arXiv Apr 16, 2026
Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical performance paradox: unimodal baselines frequently outperform joint multimodal inference. We trace t... read more 
Computer Vision and Pattern Recognition

Design and Validation of a Low-Cost Smartphone Based Fluorescence Detection Platform Compared with Conventional Microplate Readers

arXiv Apr 16, 2026
A low cost fluorescence-based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A specifically designed device setup compatible with conventional 96 well plates is chosen to create... read more 
Computer Vision and Pattern Recognition eess.IV eess.SY

CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction

arXiv Apr 16, 2026
Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive care. However, short-window sepsis prediction remains challenging, because shorter observation windows... read more 
Machine Learning cs.AI

An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring

arXiv Apr 16, 2026
Purpose. Athlete monitoring is constrained by small cohorts, heterogeneous biomarker scales, limited feasibility of repeated sampling, and the lack of reliable injury ground truth. These limitations reduce the interpretability and utility of traditio... read more 
Machine Learning stat.AP
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