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Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model.

Sensors (Basel, Switzerland)
Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell's equations and numerical models of the body are used for this p...

Robust deep learning object recognition models rely on low frequency information in natural images.

PLoS computational biology
Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robus...

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

Journal of sport and health science
BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are...

Head and neck reconstruction in the vessel depleted neck using robot-assisted harvesting of the internal mammary vessels.

The British journal of oral & maxillofacial surgery
We report a novel technique of robot-assisted harvesting of the internal mammary vessels to provide effective recipient vessels in a patient with bilateral vessel depleted neck (VDN). A 44-year-old with a Notani grade III osteoradionecrosis (ORN) of ...

Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.

Scientific reports
Perception of social stimuli (faces and bodies) relies on "holistic" (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving inverted faces/bodies is harder than perceiving their upright counterpart. Albeit neuroimaging evidenc...

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.

Journal of applied clinical medical physics
PURPOSE: In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metri...

Deep learning-based local SAR prediction using B maps and structural MRI of the head for parallel transmission at 7 T.

Magnetic resonance in medicine
PURPOSE: To predict subject-specific local specific absorption rate (SAR) distributions of the human head for parallel transmission (pTx) systems at 7 T.

Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: Expert Panel Narrative Review.

AJR. American journal of roentgenology
Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MR...

Neural Networks for Navigation: From Connections to Computations.

Annual review of neuroscience
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and r...

MHL-Net: A Multistage Hierarchical Learning Network for Head and Neck Multiorgan Segmentation.

IEEE journal of biomedical and health informatics
Accurate segmentation of head and neck organs at risk is crucial in radiotherapy. However, the existing methods suffer from incomplete feature mining, insufficient information utilization, and difficulty in simultaneously improving the performance of...