AIMC Topic: Neural Networks, Computer

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Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks.

Computational intelligence and neuroscience
A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can store multilevel information, such as image data. Storage capacity is an important problem of Hopfield neural networks. Jankowski et al. approximated...

Learning deep similarity metric for 3D MR-TRUS image registration.

International journal of computer assisted radiology and surgery
PURPOSE: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. H...

Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network.

Journal of medical systems
Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be...

Structural, dynamical and symbolic observability: From dynamical systems to networks.

PloS one
Classical definitions of observability classify a system as either being observable or not. Observability has been recognized as an important feature to study complex networks, and as for dynamical systems the focus has been on determining conditions...

Artificial neural networks and player recruitment in professional soccer.

PloS one
The aim was to objectively identify key performance indicators in professional soccer that influence outfield players' league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players' (mean S...

A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure.

International journal of neural systems
This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to ...

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.

IEEE transactions on medical imaging
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-fo...

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detec...

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks.

Australasian physical & engineering sciences in medicine
Two systems are presented for segmentation of vertebrae in a 3D computed tomography (CT) image. The first method extracts seven features from each voxel and uses a multi-layer perceptron neural network (MLPNN) to classify the voxel as vertebrae or ba...

Analyzing complex single-molecule emission patterns with deep learning.

Nature methods
A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to retrieve such information with high accura...