AIMC Topic: Neural Networks, Computer

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Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network.

Journal of healthcare engineering
Breast cancer is the most common cancer among women worldwide with about half a million cases reported each year. Mammary thermography can offer early diagnosis at low cost if adequate thermographic images of the breasts are taken. The identification...

Modeling of Brain-Like Concept Coding with Adulthood Neurogenesis in the Dentate Gyrus.

Computational intelligence and neuroscience
Mammalian brains respond to new concepts via a type of neural coding termed "concept coding." During concept coding, the dentate gyrus (DG) plays a vital role in pattern separation and pattern integration of concepts because it is a brain region with...

A neural network approach to segment brain blood vessels in digital subtraction angiography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and ...

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline.

Computer methods and programs in biomedicine
The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive interve...

Perceptrons from memristors.

Neural networks : the official journal of the International Neural Network Society
Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for ...

Multistability of switched neural networks with sigmoidal activation functions under state-dependent switching.

Neural networks : the official journal of the International Neural Network Society
This paper presents theoretical results on the multistability of switched neural networks with commonly used sigmoidal activation functions under state-dependent switching. The multistability analysis with such an activation function is difficult bec...

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

NeuroImage
MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely availab...

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

Computational and mathematical methods in medicine
As a large amount of genetic data are accumulated, an effective analytical method and a significant interpretation are required. Recently, various methods of machine learning have emerged to process genetic data. In addition, machine learning analysi...

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

IEEE journal of biomedical and health informatics
3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning m...

An iterative multi-path fully convolutional neural network for automatic cardiac segmentation in cine MR images.

Medical physics
PURPOSE: Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information ...