AIMC Topic: Neural Networks, Computer

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Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images.

Medical physics
BACKGROUND: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT).

Improvement of multi-task learning by data enrichment: application for drug discovery.

Journal of computer-aided molecular design
Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investi...

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

Ultrasonics
The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite compli...

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos.

Sensors (Basel, Switzerland)
Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV)...

An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.

Sensors (Basel, Switzerland)
This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrati...

Identifying Disease of Interest With Deep Learning Using Diagnosis Code.

Journal of Korean medical science
BACKGROUND: Autoencoder (AE) is one of the deep learning techniques that uses an artificial neural network to reconstruct its input data in the output layer. We constructed a novel supervised AE model and tested its performance in the prediction of a...

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

International journal of medical informatics
BACKGROUND AND AIMS: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic...

Optimization of biocementation responses by artificial neural network and random forest in comparison to response surface methodology.

Environmental science and pollution research international
In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random fore...

Hepatic vessels segmentation using deep learning and preprocessing enhancement.

Journal of applied clinical medical physics
PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of su...

Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods.

Biometrical journal. Biometrische Zeitschrift
When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction and differential equat...