Artificial Intelligence Medical Compendium

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

Showing 20,721 to 20,730 of 216,088 articles

[Artificial intelligence in pediatric ophthalmology: applications and perspectives].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology May 11, 2026
Pediatric visual health is not only inextricably linked to an individual's lifelong cognitive development and quality of life, but also constitutes a major public health issue concerning the quality of the national population and the long-term develo... read more 

Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis.

Biomedizinische Technik. Biomedical engineering May 11, 2026
OBJECTIVES: The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS complex. Hence, this study aims to witness Ta wave within QRS complex. METHODS: 10 s ECGs of 50 Sin... read more 

Comparative oncology of male and female breast cancer: diagnostic paradigms and machine learning approaches in treatment.

Journal of basic and clinical physiology and pharmacology May 11, 2026
Breast cancer is associated mostly with women; however, breast cancer also appears in men, which dictates the need to know about gender-specific differences in the pathology and treatment. Male breast cancer constitutes less than 1 % of all cases and... read more 

3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects

arXiv May 11, 2026
Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces still remains notoriously challenging. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency an... read more 
Computer Vision and Pattern Recognition

VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection

arXiv May 11, 2026
Privacy protection has become a critical requirement in the era of ubiquitous visual data sharing, imposing higher demands on efficient and robust privacy detection algorithms. However, current robust detection models are severely hindered by the lac... read more 
Computer Vision and Pattern Recognition cs.CY

AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting

arXiv May 11, 2026
This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply complex, architecture-specific designs on top of the generic pipeline of image feature extraction $\ri... read more 
Computer Vision and Pattern Recognition

Increasing the Efficiency of DETR for Maritime High-Resolution Images

arXiv May 11, 2026
Maritime object detection is critical for the safe navigation of unmanned surface vessels (USVs), requiring accurate recognition of obstacles from small buoys to large vessels. Real-time detection is challenging due to long distances, small object si... read more 
Computer Vision and Pattern Recognition cs.RO

PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction

arXiv May 11, 2026
Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization... read more 
Computer Vision and Pattern Recognition

Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study

arXiv May 11, 2026
Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially complement patient stratification strategies. We aim to develop and analytically validate radiologi... read more 
Machine Learning

Scalable Gaussian process inference via neural feature maps

arXiv May 11, 2026
We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the learned feature map can be interpreted as an optimal low-rank approximation to a Gram matrix derived f... read more 
Machine Learning cs.LG
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