AIMC Topic: Deep Learning

Clear Filters Showing 13761 to 13770 of 28423 articles

Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Anaplastic lymphoma kinase (ALK) and ROS oncogene 1 (ROS1) gene fusions are well-established key players in non-small cell lung cancer (NSCLC). Although their frequency is relatively low, their detection is important for patient care and guides thera...

Visual Detection and Image Processing of Parking Space Based on Deep Learning.

Sensors (Basel, Switzerland)
The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking spac...

Aurora Image Classification with Deep Metric Learning.

Sensors (Basel, Switzerland)
In recent years, neural networks have been increasingly used for classifying aurora images. In particular, convolutional neural networks have been actively studied. However, there are not many studies on the application of deep learning techniques th...

Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning.

Sensors (Basel, Switzerland)
Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have s...

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.

Zeitschrift fur medizinische Physik
INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial...

Automated segmentation of five different body tissues on computed tomography using deep learning.

Medical physics
PURPOSE: To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tiss...

Automated Calculation of Liquid Crystal Sensing Images Based on Deep Learning.

Analytical chemistry
Liquid crystal (LC)-based sensors have been extensively applied in the detection of chemical and biological events. However, the calculation of the optical images of the LC-based sensors is usually time-consuming and also might bring some errors due ...

A novel scaled-gamma-tanh (SGT) activation function in 3D CNN applied for MRI classification.

Scientific reports
Activation functions in the neural network are responsible for 'firing' the nodes in it. In a deep neural network they 'activate' the features to reduce feature redundancy and learn the complex pattern by adding non-linearity in the network to learn ...

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.

Nature communications
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applie...

Detection Method of Athlete Joint Injury Based on Deep Learning Model.

Computational and mathematical methods in medicine
The research on accurate and intelligent segmentation of knee joint MRI images is of great significance to reduce the work intensity of clinical doctors and nurses. In order to solve the problem that knee joint MRI image segmentation model needs a la...