Pulse-coupled neural networks for medical image analysis

1999 ◽  
Author(s):  
Paul E. Keller ◽  
A. D. McKinnon
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

scholarly journals Medical Image Analysis using Convolutional Neural Networks: A Review

2018 ◽  
Vol 42 (11) ◽  
Author(s):  
Syed Muhammad Anwar ◽  
Muhammad Majid ◽  
Adnan Qayyum ◽  
Muhammad Awais ◽  
Majdi Alnowami ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Medical Image Analysis

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

2021 ◽  
Author(s):  
◽  
Mahdieh Shabanian ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Neurological Disease ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Disease State ◽  
Institutional Review Board ◽  
Institutional Review

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

scholarly journals A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2132
Author(s):  
Kyriakos D. Apostolidis ◽  
George A. Papakostas
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Language Processing ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Attack Detection ◽  
Detection Methods ◽  
Proper Function ◽  
Magnetic Resonance Imaging Mri

In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. In this paper, we summarize existing methods for adversarial attacks, detections and defenses on medical imaging. Finally, we show that many attacks, which are undetectable by the human eye, can degrade the performance of the models, significantly. Nevertheless, some effective defense and attack detection methods keep the models safe to an extent. We end with a discussion on the current state-of-the-art and future challenges.

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