Artificial Intelligence Medical Compendium

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

Showing 33,251 to 33,260 of 221,422 articles

T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation

arXiv Apr 9, 2026
Medical image segmentation traditionally relies on fully supervised 3D architectures that demand a large amount of dense, voxel-level annotations from clinical experts which is a prohibitively expensive process. Vision Language Models (VLMs) offer a ... read more 
Computer Vision and Pattern Recognition

On the Global Photometric Alignment for Low-Level Vision

arXiv Apr 9, 2026
Supervised low-level vision models rely on pixel-wise losses against paired references, yet paired training sets exhibit per-pair photometric inconsistency, say, different image pairs demand different global brightness, color, or white-balance mappin... read more 
Computer Vision and Pattern Recognition

MedVR: Annotation-Free Medical Visual Reasoning via Agentic Reinforcement Learning

arXiv Apr 9, 2026
Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-only paradigms that fail to ground inferences in visual evidence. This limitation not only curtails ... read more 
Computer Vision and Pattern Recognition cs.AI

Introducing Echo Networks for Computational Neuroevolution

arXiv Apr 9, 2026
For applications on the extreme edge, minimal networks of only a few dozen artificial neurons for event detection and classification in discrete time signals would be highly desirable. Feed-forward networks, RNNs, and CNNs evolved through evolutionar... read more 
Machine Learning cs.NE

SciFigDetect: A Benchmark for AI-Generated Scientific Figure Detection

arXiv Apr 9, 2026
Modern multimodal generators can now produce scientific figures at near-publishable quality, creating a new challenge for visual forensics and research integrity. Unlike conventional AI-generated natural images, scientific figures are structured, tex... read more 
Computer Vision and Pattern Recognition

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment

arXiv Apr 9, 2026
General-purpose vision-language models demonstrate strong performance in everyday domains but struggle with specialized technical fields requiring precise terminology, structured reasoning, and adherence to engineering standards. This work addresses ... read more 
Computer Vision and Pattern Recognition

EditCaption: Human-Aligned Instruction Synthesis for Image Editing via Supervised Fine-Tuning and Direct Preference Optimization

arXiv Apr 9, 2026
High-quality training triplets (source-target image pairs with precise editing instructions) are a critical bottleneck for scaling instruction-guided image editing models. Vision-language models (VLMs) are widely used for automated instruction synthe... read more 
Computer Vision and Pattern Recognition cs.AI

$\oslash$ Source Models Leak What They Shouldn't $\nrightarrow$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization

arXiv Apr 9, 2026
The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may inadvertently retain and leak sensitive source-domain specific information in the target domain. T... read more 
Computer Vision and Pattern Recognition

DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection

arXiv Apr 9, 2026
The complex and dynamic real-world clinical environment demands reliable deep learning (DL) systems. Out-of-distribution (OOD) detection plays a critical role in enhancing the reliability and generalizability of DL models when encountering data that ... read more 
Computer Vision and Pattern Recognition cs.AI

Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising

arXiv Apr 9, 2026
Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However, DIP-based methods are inherently prone to overfitting, which leads to performance degradation and ... read more 
Computer Vision and Pattern Recognition eess.IV
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