Artificial Intelligence Medical Compendium

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

Showing 16,211 to 16,220 of 213,568 articles

Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks

arXiv May 21, 2026
Accurate detection and counting of virus patches in focus-forming unit (FFU) images, also known as foci images, are important for quantifying viral infection and analyzing cellular structures. This task is challenging because biomedical targets often... read more 
Computer Vision and Pattern Recognition

PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

arXiv May 21, 2026
Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as models may continue to synthesize individuals despite their right to be forgotten. While machine unlear... read more 
Computer Vision and Pattern Recognition

Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning

arXiv May 21, 2026
Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when data are accelerated or noisy. Materials and Methods: This retrospective study used public breast... read more 
Computer Vision and Pattern Recognition physics.med-ph

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

arXiv May 21, 2026
Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by systems-level barriers: heterogeneous data representations, the absence of standardized deployment work... read more 
Machine Learning cs.AI cs.DC

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces

arXiv May 21, 2026
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curved geometry of correlation space. Existing geometric methods frequently lack closed-form operations... read more 
Machine Learning

Bernini: Latent Semantic Planning for Video Diffusion

arXiv May 21, 2026
Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos wit... read more 
Computer Vision and Pattern Recognition cs.AI cs.MM

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks

arXiv May 21, 2026
Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR models are pushed to ultra-low precision (2-4 bits), performance can dr... read more 
Computer Vision and Pattern Recognition

VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography

arXiv May 21, 2026
Accurate segmentation of hepatic and portal vessels in contrast-enhanced computed tomography angiography (CTA) remains challenging due to complex vascular topology, peripheral visibility limitations, and acquisition-induced ambiguities. While existin... read more 
Computer Vision and Pattern Recognition cs.AI

AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture

arXiv May 21, 2026
Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multimodal benchmarks mainly evaluate final-answer correctness and provide ... read more 
Computer Vision and Pattern Recognition

ASAP: Attention Sink Anchored Pruning

arXiv May 21, 2026
Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token reduction methods rely on local metrics - such as single-layer attention scores - that are inherent... read more 
Machine Learning
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