Artificial Intelligence Medical Compendium

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

Showing 11,831 to 11,840 of 210,187 articles

Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening

arXiv May 25, 2026
Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in realistic multi-disease settings and under domain shift. In this work, we benchmark twelve architectures... read more 
Computer Vision and Pattern Recognition cs.LG

A multifractal-based masked auto-encoder: an application to medical images

arXiv May 25, 2026
Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs may overlook critical areas in medical images, where even subtle changes can indicate disease. To ad... read more 
Computer Vision and Pattern Recognition

Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning

arXiv May 25, 2026
Parameter-efficient adaptation of vision-language foundation models is crucial for precise multimodal understanding of biomedical images, yet existing methods remain deterministic and often struggle under domain shift or ambiguous image-text alignmen... read more 
Computer Vision and Pattern Recognition cs.CL

CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection

arXiv May 25, 2026
Skin cancer is a common and fast rising malignancy worldwide. Early detection is critical for improving outcomes. Deep learning models trained on dermoscopic and clinical images can support automated and fast triage. However, many studies evaluate on... read more 
Computer Vision and Pattern Recognition

Sleep-stage efficient classification using a lightweight self-supervised model

arXiv May 25, 2026
Accurate classification of sleep stages is crucial for diagnosing sleep disorders and automating this process can significantly enhance clinical assessments. This study aims to explore the use of a self-supervised model (more specifically, an adapted... read more 
Computer Vision and Pattern Recognition

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

arXiv May 25, 2026
The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrate multiple data modalities beyond text and images. In seismology, unde... read more 
Machine Learning cs.CL

RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields

arXiv May 25, 2026
Radars are an ideal complement to cameras: both are inexpensive, solid-state sensors, with cameras offering fine angular resolution, while radars provide metric depth and robustness under adverse weather. However, radar data is more difficult to inte... read more 
Computer Vision and Pattern Recognition

NightSight: Passive Computation for Navigation in Dark Using Events

arXiv May 25, 2026
Small aerial robots are particularly well-suited for search and rescue in confined and hazardous environments due to their agility, low cost, and ability to traverse through cluttered spaces that are inaccessible to larger platforms. However, enablin... read more 
Robotics

Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models

arXiv May 25, 2026
Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model generate such unlearned concepts. These attacks, nevertheless, do not ... read more 
Computer Vision and Pattern Recognition cs.AI

Personalized Generative Models for Contextual Debiasing

arXiv May 25, 2026
Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These statistics are reflected in vision datasets, and as a result trained models more easily recognize obje... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG
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