Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks / 生物医学工程学杂志

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.

Clasificación de Imágenes de Neumonía a causa de Covid-19 utilizando Transfer-Learning basado en Redes Convolucionales / Classification of Images of Pneumonia Due to Covid-19 Using TransferLearning, Based on Convolutional Networks

La Inteligencia Artificial ha ayudado a lidiar diferentes problemas relacionados con los datos masivos y a su vez con su tratamiento, diagnóstico y detección de enfermedades como la que actualmente nos preocupa, la Covid-19. El objetivo de esta investigación ha sido analizar y desarrollar la clasificación de imágenes de neumonía a causa de covid-19 para un diagnostico efectivo y óptimo. Se ha usado Transfer-Learning aplicando ResNet, DenseNet, Poling y Dense layer para la elaboración de los modelos de red propios Covid-UPeU y Covid-UPeU-TL, utilizando las plataformas Kaggle y Google colab, donde se realizaron 4 experimentos. El resultado con una mejor clasificación de imágenes se obtuvo en el experimento 4 prueba N°2 con el modelo Covid-UPeU-TL donde Acc.Train: 0.9664 y Acc.Test: 0.9851. Los modelos implementados han sido desarrollados con el propósito de tener una visión holística de los factores para la optimización en la clasificación de imágenes de neumonía a causa de COVID-19(AU)

Artificial Intelligence has helped to deal with different problems related to massive data in turn to the treatment, diagnosis and detection of diseases such as the one that currently has us in concern, Covid-19. The objective of this research has been to analyze and develop the classification of images of pneumonia due to covid-19 for an effective and optimal diagnosis. Transfer-Learning has been used applying ResNet, DenseNet, Poling and Dense layer for the elaboration of the own network models Covid-Upeu and Covid-UpeU-TL, using Kaggle and Google colab platforms, where 4 experiments have been carried out. The result with a better classification of images was obtained in experiment 4 test N ° 2 with the Covid-UPeU-TL model where Acc.Train: 0.9664 and Acc.Test: 0.9851. The implemented models have been developed with the purpose of having a holistic view of the factors for optimization in the classification of COVID-19 images(AU)

Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks / 中国医疗器械杂志

OBJECTIVE@#Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.@*METHODS@#Enlightened by the advantages of convolutional neural networks on features extraction, fully convolutional networks consisting of 9 layers were designed to segment medical images. The networks used convolution kernels of various sizes to extract multi-dimensional image features in the images, meanwhile, eliminated pooling layers to avoid the loss of image details during downsampling procedures.@*RESULTS@#The experiment was conducted in accordance with the specific scene of X-ray images segmentation. Compared with traditional segmentation methods, this approach achieved more accurate segmentation of anatomical areas.@*CONCLUSIONS@#Fully convolutional networks can extract representative and multidimensional features of medical images, avoid the loss of image details during downsampling procedures, and complete automatic segmentation of anatomical areas accurately in X-ray images.

Research progress and challenges of deep learning in medical image registration / 生物医学工程学杂志

With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.