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Automatic segmentation of the liver in abdominal CT images is critical for guiding liver cancer biopsies and treatment planning. Yet, automatic segmentation of CT liver images remains challenging due to the poor contrast between the liver and surrounding organs in abdominal CT images. In this paper, we propose a novel network for liver segmentation, and the network is essentially a U-shaped network with an encoder–decoder structure. Firstly, the complementary feature enhancement unit is designed in the network to mitigate the semantic gap between encoder and decoder. The complementary feature enhancement unit is based on subtraction, which enhances the complementary features between encoder and decoder. Secondly, this paper proposes a new cross attention model that no longer generates value by convolution, which reduces redundant information and enhances the contextual information of single sparse attention by encoding contextual information by 3 × 3 convolution. The dice score, accuracy, and precision of our network on the LiTS dataset were 95.85 % , 97.19 % , and 97.11 % , and the dice score, accuracy, and precision on the dataset consisted of 3Dircadb and CHAOS were 93.65 % , 94.38 % , and 97.53 % . [ABSTRACT FROM AUTHOR]

This paper presents a simple, efficient, yet robust approach, named joint-scale local binary pattern (JLBP), for texture classification. In the proposed approach, the joint-scale strategy is developed firstly, and the neighborhoods of different scales are fused together by a simple arithmetic operation. And then, the descriptor is extracted from the mutual integration of the local patches based on the conventional local binary pattern (LBP). The proposed scheme can not only describe the micro-textures of a local structure, but also the macro-textures of a larger area because of the joint of multiple scales. Further, motivated by the completed local binary pattern (CLBP) scheme, the completed JLBP (CJLBP) is presented to enhance its power. The proposed descriptor is evaluated in relation to other recent LBP-based patterns and non-LBP methods on popular benchmark texture databases, Outex, CURet and UIUC. Generally, the experimental results show that the new method performs better than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection. [ABSTRACT FROM AUTHOR]

The brain lesions images of Alzheimer's disease (AD) patients are slightly different from the Magnetic Resonance Imaging of normal people, and the classification effect of general image recognition technology is not ideal. Alzheimer's datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism. The weakly supervised data augmentation network is used as the basic model, the attention map generated by weakly supervised learning is used to guide the data augmentation, and an attention module with channel domain and spatial domain is embedded in the residual network to focus on the distinctive channels and spaces of images respectively. The location information enhances the corresponding features of related features and suppresses the influence of irrelevant features.The results show that the F1-score is 99.63%, the accuracy is 99.61%. Our model provides a high-performance solution for accurate classification of AD. [ABSTRACT FROM AUTHOR]

Abstract: Color histogram is an important technique for color image database indexing and retrieving. However, the main problem with color histogram indexing is that it does not take the color spatial distribution into consideration. Previous researches have proved that the effectiveness of image retrieval increases when spatial feature of colors is included in image retrieval. In this paper, two new descriptors, color distribution entropy (CDE) and improved CDE (I-CDE), which introduce entropy to describe the spatial information of colors, are presented. In comparison with the spatial chromatic histogram (SCH) and geostat which also measure the global spatial relationship of colors, the experiment results show that CDE and I-CDE give better performance than SCH and geostat. [Copyright &y& Elsevier]

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison. • We propose using spectral clustering to group the features so that the in-group feature similarity is extremely high. • We propose a redundant feature filter algorithm since existing algorithms cannot filter redundant features. • we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets. [ABSTRACT FROM AUTHOR]

Abstract: The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method for image retrieval in large database. Some extensions of VA-file have been proposed towards better query performance. However, all of these methods applying sequential scan need read the whole vector approximation file. In this paper, we present a new indexing structure based on vector approximation method, in which only a small part of approximation file need be accessed. First, principal component analysis is used to map multidimensional points to a 1D line. Then a B +-tree is built to index the approximate vector according to principal component. When performing k-nearest neighbor search, the partial distortion searching algorithm is used to reject the improper approximate vectors. Only a small set of approximate vectors need to be sequentially scanned during the search, which can reduce the CPU cost and I/O cost dramatically. Experiment results on large image databases show that the new approach provides a faster search speed than the other VA-file approaches. [Copyright &y& Elsevier]

Background: The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system.Objective: In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images.Methods: First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier.Results: Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed.Conclusion: In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease. [ABSTRACT FROM AUTHOR]