AIMC Topic: Neural Networks, Computer

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High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.

BMC medical imaging
BACKGROUND: Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radi...

Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Face hallucination or super-resolution is a practical application of general image super-resolution which has been recently studied by many researchers. The challenge of good face hallucination comes from a variety of poses, illuminations, facial exp...

Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks.

Methods (San Diego, Calif.)
This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method. The first benchmark based on the microscopic hyperspectral pathological images is set up. Particula...

Multi-periodicity of switched neural networks with time delays and periodic external inputs under stochastic disturbances.

Neural networks : the official journal of the International Neural Network Society
This paper presents new theoretical results on the multi-periodicity of recurrent neural networks with time delays evoked by periodic inputs under stochastic disturbances and state-dependent switching. Based on the geometric properties of activation ...

i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.

Interdisciplinary sciences, computational life sciences
DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Bioch...

Multistability of delayed fractional-order competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the multistability of fractional-order competitive neural networks (FCNNs) with time-varying delays. Based on the division of state space, the equilibrium points (EPs) of FCNNs are given. Several sufficient conditions and...

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel ...

EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images.

Artificial intelligence in medicine
This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradig...

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network.

Biomolecules
Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze...

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Sensors (Basel, Switzerland)
Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified ...