Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation

Deng, Hongxia; Zhang, Yuefang; Li, Ran; Hu, Chunxiang; Feng, Zijian; Li, Haifang

doi:10.26599/tst.2020.9010056

Cited by 21 publications

(11 citation statements)

References 28 publications

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“…Our review of the literature [8][9][10][11][12][13][14] revealed the following shortcomings in the above models (methods):…”

Section: Improving a Neural Network Model For Semantic Segmentation Of Images Of Monitored Objects In Aerial Photographsmentioning

confidence: 99%

“…Study [9] proposed various models based on convolutional neural networks (CNNs) to collect information obtained using a segmentation network; a generative adversarial network based on Pixel2Pixel was suggested. The discriminator employed CNN to distinguish between the results of segmentation of the generated model and the Expert Advisor.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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This paper considers a model of the neural network for semantically segmenting the images of monitored objects on aerial photographs. Unmanned aerial vehicles monitor objects by analyzing (processing) aerial photographs and video streams. The results of aerial photography are processed by the operator in a manual mode; however, there are objective difficulties associated with the operator's handling a large number of aerial photographs, which is why it is advisable to automate this process. Analysis of the models showed that to perform the task of semantic segmentation of images of monitored objects on aerial photographs, the U-Net model (Germany), which is a convolutional neural network, is most suitable as a basic model. This model has been improved by using a wavelet layer and the optimal values of the model training parameters: speed (step) ‒ 0.001, the number of epochs ‒ 60, the optimization algorithm ‒ Adam. The training was conducted by a set of segmented images acquired from aerial photographs (with a resolution of 6,000×4,000 pixels) by the Image Labeler software in the mathematical programming environment MATLAB R2020b (USA). As a result, a new model for semantically segmenting the images of monitored objects on aerial photographs with the proposed name U-NetWavelet was built. The effectiveness of the improved model was investigated using an example of processing 80 aerial photographs. The accuracy, sensitivity, and segmentation error were selected as the main indicators of the model's efficiency. The use of a modified wavelet layer has made it possible to adapt the size of an aerial photograph to the parameters of the input layer of the neural network, to improve the efficiency of image segmentation in aerial photographs; the application of a convolutional neural network has allowed this process to be automatic.

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“…Our review of the literature [8][9][10][11][12][13][14] revealed the following shortcomings in the above models (methods):…”

Section: Improving a Neural Network Model For Semantic Segmentation Of Images Of Monitored Objects In Aerial Photographsmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

View full text Add to dashboard Cite

show abstract

“…It also reduces costs, and energy consumption, etc [4].To sum up, CNN is an organic combination of neural network and traditional depth machine learning technology. It can conduct adaptive training on images, so that its weight in convolutional neural network can be effectively improved [5]. CNN is superior to traditional neural networks in fault tolerance, adaptability, self-learning ability, etc.…”

Section: Introductionmentioning

confidence: 99%

An improved CNN algorithm for accelerating structural optimization with pulsating array

Xiao

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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The rapid development of artificial intelligence has prompted the convolutional neural network (CNN) to process huge amount of data, which has caused a great burden on convolution operations. Therefore, according to the characteristics of the systolic array architecture, the acceleration structure of CNN is constructed by fusing it with CNN. Besides, it is optimized in practical application, and its effectiveness is verified. The experimental results show that in the broadcast architecture, the time required by the CNN acceleration architecture is at least 0.005, while the maximum throughput is 16.83, which is far higher than the acceleration architecture under the systolic array architecture. In the case of small change in the maximum frequency, the error rate is the same as that of the systolic array, which is about 3.62%. In the comparison of various methods proposed on the systolic array, the accuracy rate of CNN acceleration architecture is 94.7%, and the utilization rate is 81.95%. The correctness and effectiveness of the algorithm are proved. To sum up, the improved CNN acceleration structure based on pulse array optimization reduces the response time and meets the requirements of terminal calculation force, which is of high significance in practical application.

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“…Moreover, it is expected to decrease the workload on medical professionals and reduce the frequency of patient visit and waiting time. Many recent studies ( Basaia et al, 2019 ; Bi et al, 2020 ; Jiang et al, 2020 ; Guo et al, 2021 ; Hett et al, 2021 ; Mehdipour Ghazi et al, 2021 ; Deng et al, 2022 ) have been conducted to forecast early stages of Alzheimer’s disease. The goal of this study is to build a computer-aided system based on a deep learning algorithm to evaluate the pathological brain structural changes in MRI data in order to forecast the early stages of Alzheimer’s disease before it progresses to the severe stages.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Lim

Lai

Haiskin

et al. 2022

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer’s disease permits the provision of preventive medication to slow the disease’s progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer’s disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.

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Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation

Cited by 21 publications

References 28 publications

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

An improved CNN algorithm for accelerating structural optimization with pulsating array

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

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