A 2D/3D Convolutional Neural Network for Brain White Matter Lesion Detection in Multimodal MRI

2017 ◽  
pp. 377-385 ◽  
Author(s):  
Leire Roa-Barco ◽  
Oscar Serradilla-Casado ◽  
Mikel de Velasco-Vázquez ◽  
Asier López-Zorrilla ◽  
Manuel Graña ◽  
...  
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Convolutional Neural Network ◽  
White Matter Lesion ◽  
Lesion Detection ◽  
Brain White Matter ◽  
Multimodal Mri

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network

2018 ◽  
Vol 167 ◽  
pp. 49-63 ◽  
Author(s):  
Pedro Henrique Bandeira Diniz ◽  
Thales Levi Azevedo Valente ◽  
João Otávio Bandeira Diniz ◽  
Aristófanes Corrêa Silva ◽  
Marcelo Gattass ◽  
...  
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Convolutional Neural Network ◽  
White Matter Lesion

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

Time Resolved optical detection for white matter lesion detection: preclinical tests on macaque brains and MRI co-registration.

2015 ◽  
Author(s):  
A. Planat-Chrétien ◽  
M. Berger ◽  
L. Hervé ◽  
L. Watroba ◽  
J. Demilly ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesion ◽  
Optical Detection ◽  
Lesion Detection ◽  
Time Resolved

scholarly journals Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0195798 ◽  
Author(s):  
Paul Blanc-Durand ◽  
Axel Van Der Gucht ◽  
Niklaus Schaefer ◽  
Emmanuel Itti ◽  
John O. Prior
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lesion Detection ◽  
Fet Pet

Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection

2018 ◽  
Vol 22 (20) ◽  
pp. 6825-6833 ◽  
Author(s):  
Tien-Duong Vu ◽  
Ngoc-Huynh Ho ◽  
Hyung-Jeong Yang ◽  
Jahae Kim ◽  
Ho-Chun Song
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Disease Detection ◽  
Tissue Extraction

Small white matter lesion detection in cerebral small vessel disease

2015 ◽  
Author(s):  
Mohsen Ghafoorian ◽  
Nico Karssemeijer ◽  
Inge van Uden ◽  
Frank E. de Leeuw ◽  
Tom Heskes ◽  
...  
Keyword(s):  
White Matter ◽  
Small Vessel Disease ◽  
White Matter Lesion ◽  
Cerebral Small Vessel Disease ◽  
Lesion Detection ◽  
Small Vessel ◽  
Vessel Disease
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