Artificial Intelligence Medical Compendium

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

Showing 48,651 to 48,660 of 224,513 articles

Learning Optimal and Sample-Efficient Decision Policies with Guarantees

arXiv Feb 20, 2026
The paradigm of decision-making has been revolutionised by reinforcement learning and deep learning. Although this has led to significant progress in domains such as robotics, healthcare, and finance, the use of RL in practice is challenging, particu... read more 
Machine Learning cs.AI

From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection

arXiv Feb 20, 2026
Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radiomic features and overlook the complementary value of spatially resolved radiomic parametric maps. We... read more 
Image and Video Processing cs.CV

Image Quality Assessment: Exploring Quality Awareness via Memory-driven Distortion Patterns Matching

arXiv Feb 20, 2026
Existing full-reference image quality assessment (FR-IQA) methods achieve high-precision evaluation by analysing feature differences between reference and distorted images. However, their performance is constrained by the quality of the reference ima... read more 
Computer Vision and Pattern Recognition

Asynchronous Heavy-Tailed Optimization

arXiv Feb 20, 2026
Heavy-tailed stochastic gradient noise, commonly observed in transformer models, can destabilize the optimization process. Recent works mainly focus on developing and understanding approaches to address heavy-tailed noise in the centralized or distri... read more 
Machine Learning

Towards LLM-centric Affective Visual Customization via Efficient and Precise Emotion Manipulating

arXiv Feb 20, 2026
Previous studies on visual customization primarily rely on the objective alignment between various control signals (e.g., language, layout and canny) and the edited images, which largely ignore the subjective emotional contents, and more importantly ... read more 
Computer Vision and Pattern Recognition

UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models

arXiv Feb 20, 2026
Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demonstrating remarkable potential for generalizable robotic manipulation. To enhance performance, existing ... read more 
Computer Vision and Pattern Recognition cs.RO

Dual-Channel Attention Guidance for Training-Free Image Editing Control in Diffusion Transformers

arXiv Feb 20, 2026
Training-free control over editing intensity is a critical requirement for diffusion-based image editing models built on the Diffusion Transformer (DiT) architecture. Existing attention manipulation methods focus exclusively on the Key space to modul... read more 
Computer Vision and Pattern Recognition cs.AI

Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework

arXiv Feb 20, 2026
Dual-to-Dual MLLMs refer to Multimodal Large Language Models, which can enable unified multimodal comprehension and generation through text and image modalities. Although exhibiting strong instantaneous learning and generalization capabilities, Dual-... read more 
Machine Learning

3DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysis

arXiv Feb 20, 2026
3D CT analysis spans a continuum from low-level perception to high-level clinical understanding. Existing 3D-oriented analysis methods adopt either isolated task-specific modeling or task-agnostic end-to-end paradigms to produce one-hop outputs, impe... read more 
Computer Vision and Pattern Recognition

Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation

arXiv Feb 20, 2026
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We address this limitation with a two-phase training strategy for fine-grained ... read more 
Computer Vision and Pattern Recognition
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