AIMC Topic: Neural Networks, Computer

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A node-based informed modularity strategy to identify organizational modules in anatomical networks.

Biology open
The study of morphological modularity using anatomical networks is growing in recent years. A common strategy to find the best network partition uses community detection algorithms that optimize the modularity Q function. Because anatomical networks ...

Deep pyramid local attention neural network for cardiac structure segmentation in two-dimensional echocardiography.

Medical image analysis
Automatic semantic segmentation in 2D echocardiography is vital in clinical practice for assessing various cardiac functions and improving the diagnosis of cardiac diseases. However, two distinct problems have persisted in automatic segmentation in 2...

A multiple-channel and atrous convolution network for ultrasound image segmentation.

Medical physics
PURPOSE: Ultrasound image segmentation is a challenging task due to a low signal-to-noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, th...

Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Forensic science international
Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ...

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.

Computational and mathematical methods in medicine
Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a h...

CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems.

Neural networks : the official journal of the International Neural Network Society
The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. R...

Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network.

Accident; analysis and prevention
Pedestrian protection is an important component of road safety. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict potential traffic conflicts between pedestrians and vehicles at signalized intersecti...

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

Human brain mapping
As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, a...

Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.

Chemical biology & drug design
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...