scholarly journals Isointense infant brain segmentation with a hyper-dense connected convolutional neural network

2018 ◽  
Author(s):  
Jose Dolz ◽  
Ismail Ben Ayed ◽  
Jing Yuan ◽  
Christian Desrosiers
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Segmentation ◽  
Infant Brain

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

Multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network

2021 ◽  
pp. 82-91
Author(s):  
Wenting Duan ◽  
Lei Zhang ◽  
Jordan Colman ◽  
Giosue Gulli ◽  
Xujiong Ye
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Segmentation

Brain Segmentation Based on UNet++ with Weighted Parameters and Convolutional Neural Network

2021 ◽  
Author(s):  
Alin Hou ◽  
Lang Wu ◽  
Hongjian Sun ◽  
Qihao Yang ◽  
Hongkun Ji ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Segmentation

Decoding speech information from EEG data with 4, 7 and 11 month-old infants: Contrasting convolutional neural network, mutual information-based and backward linear models

2021 ◽  
Author(s):  
Mahmoud Keshavarzi ◽  
Áine Ní Choisdealbha ◽  
Adam Attaheri ◽  
Sinead Rocha ◽  
Perrine Brusini ◽  
...  
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Neural Activity ◽  
Computational Models ◽  
Delta Band ◽  
Infant Brain ◽  
Speech Envelope ◽  
Speech Information

Computational models that successfully translate neural activity into speech are multiplying in the adult literature, with non-linear convolutional neural network (CNN) approaches joining the more frequently-employed linear and mutual information (MI) models. Despite the promise of these methods for uncovering the neural basis of language acquisition by the human brain, similar studies with infants are rare. Existing infant studies rely on simpler cross-correlation and other linear techniques and aim only to establish neural tracking of the broadband speech envelope. Here, three novel computational models were applied to measure whether low-frequency speech envelope information was encoded in infant neural activity. Backward linear and CNN models were applied to estimate speech information from neural activity using linear versus nonlinear approaches, and a MI model measured how well the acoustic stimuli were encoded in infant neural responses. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung nursery rhymes) presented by video with a female singer. Each model computed speech information for these nursery rhymes in two different frequency bands, delta (1 – 4 Hz) and theta (4 – 8 Hz), thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band and theta-band neural activity from 4 months of age. All models also demonstrated higher accuracy for the delta-band neural response in the infant brain. However, only the linear and MI models showed developmental (age-related) effects, and these developmental effects differed by model. Accordingly, the choice of algorithm used to decode speech envelope information from neural activity in the infant brain may determine the developmental conclusions that can be drawn. Better understanding of the strengths and weaknesses of each modelling approach will be fundamental to improving our understanding of how the human brain builds a language system.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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