AIMC Topic: Neural Networks, Computer

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BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of simi...

A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

Medical physics
PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background.

Image domain dual material decomposition for dual-energy CT using butterfly network.

Medical physics
PURPOSE: Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to...

Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS.

Analytical chemistry
Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due ...

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems.

Computational intelligence and neuroscience
Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks hav...

Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots.

Computational intelligence and neuroscience
This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship o...

A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network.

Computational intelligence and neuroscience
In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is ...

Modulation Classification of Underwater Communication with Deep Learning Network.

Computational intelligence and neuroscience
Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the fiel...

A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm.

Computational intelligence and neuroscience
Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-di...

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Medical image analysis
The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimiza...