AI Medical Compendium Topic

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

Head

Showing 111 to 120 of 207 articles

Clear Filters

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Physics in medicine and biology
Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been propos...

FASHE: A FrActal Based Strategy for Head Pose Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Head pose estimation (HPE) represents a topic central to many relevant research fields and characterized by a wide application range. In particular, HPE performed using a singular RGB frame is particular suitable to be applied at best-frame-selection...

An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
INTRODUCTION: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineatio...

Head motion classification using thread-based sensor and machine learning algorithm.

Scientific reports
Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and cla...

Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.

Journal of neurointerventional surgery
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on v...

A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.

Scientific reports
Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, tr...

Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots.

Sensors (Basel, Switzerland)
Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assista...

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Journal of digital imaging
Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility...

Performance Analysis of a Head and Eye Motion-Based Control Interface for Assistive Robots.

Sensors (Basel, Switzerland)
Assistive robots support people with limited mobility in their everyday life activities and work. However, most of the assistive systems and technologies for supporting eating and drinking require a residual mobility in arms or hands. For people with...

Hier R-CNN: Instance-Level Human Parts Detection and A New Benchmark.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Detecting human parts at instance-level is an essential prerequisite for the analysis of human keypoints, actions, and attributes. Nonetheless, there is a lack of a large-scale, rich-annotated dataset for human parts detection. We fill in the gap by ...