AIMC Topic: Deep Learning

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Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.

Japanese journal of radiology
PURPOSE: To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists.

Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.

Medical physics
PURPOSE: Image guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular concern in children who need be treated per Pediatric Image Gently Protocols due to long-term risks...

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.

Scientific reports
COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to c...

External validation study on the value of deep learning algorithm for the prediction of hematoma expansion from noncontrast CT scans.

BMC medical imaging
BACKGROUND: Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options. This study aims to explore the value of a deep learning algori...

Automated detection of pulmonary embolism from CT-angiograms using deep learning.

BMC medical imaging
BACKGROUND: The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data.

Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images.

PLoS computational biology
Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep...

Web Page Classification Algorithm Based on Deep Learning.

Computational intelligence and neuroscience
Transmit and process information to establish a learning mechanism and realize the processing of image data and sound data. However, the current research on Web page classification algorithm (WPCA) based on deep learning (DL) is not in-depth. Therefo...

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information.

PeerJ
Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based o...

A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS
INTRODUCTION: The current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real cou...