AIMC Topic: Algorithms

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Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).

Magnetic resonance in medicine
PURPOSE: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TI ) to suppress the background my...

Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.

Computational intelligence and neuroscience
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguis...

Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes.

Computational intelligence and neuroscience
Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical proces...

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Medical image analysis
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example...

Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors.

IEEE journal of biomedical and health informatics
The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameter...

Deep associative neural network for associative memory based on unsupervised representation learning.

Neural networks : the official journal of the International Neural Network Society
This paper presents a deep associative neural network (DANN) based on unsupervised representation learning for associative memory. In brain, the knowledge is learnt by associating different types of sensory data, such as image and voice. The associat...

Fixed-time synchronization of quaternion-valued memristive neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
In this paper, a new type of neural networks, quaternion-valued memristive neural networks (QVMNNs) is formulated. On the basis of the differential inclusion principle and the Lyapunov functional method, fixed-time synchronization problem is consider...

Gain determination of feedback force for an ultrasound scanning robot using genetic algorithm.

International journal of computer assisted radiology and surgery
PURPOSE: The remote medical diagnosis system (RMDS) is for providing medical diagnosis to the patients located in remote sites. To apply to RMDS and medical automation, many master-slave type ultrasound scanning robots are being developed and researc...

Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol ...

A stochastic variational framework for Recurrent Gaussian Processes models.

Neural networks : the official journal of the International Neural Network Society
Gaussian Processes (GPs) models have been successfully applied to the problem of learning from sequential observations. In such context, the family of Recurrent Gaussian Processes (RGPs) have been recently introduced with a specifically designed stru...