Finger vein recognition has become one of the most popular research hotpots in the field of biometrics because of its unique technical advantages such as living body recognition,high security and inner features.Firstly,this paper introduces the principle,merits,and current research status of finger vein recognition,then making the time as the clue,sorts out the development history of finger vein recognition technology,and discusses the classical and state-of-the-art recognition algorithms.Secondly,focusing on each process of finger vein recognition,this paper expounds on the critical techniques including image acquisition,image preprocessing,feature extraction and matching in traditional methods,and deep learning-based recognition.Besides,the commonly used public datasets and the related evaluation metrics in this field are introduced.Thirdly,this paper summarizes the existing research problems,proposes the corresponding feasible solutions,and predicts the future research direction of finger vein recognition.Some new ideas in the following studies for researchers are provided at the end.

Brain tumors are the most common diseases of nervous system except cerebrovascular disease,and their segmentation is also an important field in medical image processing.Accurately segmenting the tumor region is the first step in the treatment of brain tumors.Aiming at the problem of information loss caused by the weak multi-scale processing ability of traditional fully convolutional networks,a fully convolutional network based on multi-scale features is proposed.Using spatial pyramid pooling to obtain advanced features of multiple receptive fields,thereby capturing contextual multi-scale information and improving the adaptability to different scale features.Replacing the original convolution layer with the residual compact module can alleviate the degradation problem and extract more features.The data augmentation technology is combined to enhance the segmentation perfor-mance maximally while avoiding over fitting.Through a large number of contrastive ablation experiments on the public low-grade glioma MRI dataset,using Dice coefficient,Jaccard index and accuracy as the main evaluation criteria,91.8% Dice coefficient,85.0% Jaccard index and 99.5% accuracy are obtained.Experimental results show that the proposed method can effectively segment brain tumor regions and have certain generalization,and the segmentation effect is better than other networks.

The adverse reactions of drug-drug interactions have become one of the important reasons for the increase in the incidence of diseases such as digestive system diseases and cardiovascular diseases,and leads to the withdrawal of drugs from the market.Therefore,accurate prediction of drug interactions attracte widespread attention.Aiming at the problem that the traditional Encoder-Decoder model cannot capture the dependence between drug substructures,this paper proposes a TransDDI(TransformerDDI) based on Transformer and LSTM drug interaction prediction model.TransDDI includes three parts:data preprocessing module,latent feature extraction module and mapping module.The data preprocessing module uses the SPM algorithm to extract the frequent substructures that characterize the drug from the SMILES format input of the drug to form the drug feature vector,and then generate the feature vector of the drug pair.The latent feature extraction module uses Transformer to fully mine the information contained in the substructures of the feature vector,highlight the different important roles of different substructures,and generate potential feature vectors.The mapping module mainly forms a dictionary representation of the potential feature vector of the drug pair and the vector of the frequent substructure in the database,and uses the neural network fused with LSTM to make predictions.Onreal data sets BIOSNAP and DrugBank,the proposed method is compared with other 6 machine learning and deep learning methods by experiments.The results show that TransDDI has a higher accuracy rate and is convenient for drug interaction prediction.

Skin melanoma is a kind of disease that can be cured by early detection.The main diagnosis method is based on the manual visual observation of dermatoscope.Affected by the doctor's medical skill and experience,the diagnostic accuracy is 75%~80% and the diagnostic efficiency is low.In this paper,a multi-modal neural network algorithm based on metadata and image data is proposed.Metadata is the feature vector that extracts the basic information of patients,the location of lesions,the resolution and quantity of images through perceptual machine learning model.The image data is extracted from the feature vectors of CNN model,and the two feature vectors are fused and mapped to obtain the disease classification results,which can be used for early auxiliary diagnosis of melanoma.A total of 58 457 samples are collected from ISIC 2019 and ISIC 2020 mixed data sets.The training samples and test samples are divided according to the ratio of 4∶1.The multi-modal algorithm and convolutional neural network method proposed in this paper are used for comparative experimental research.The results show that the AUC value of the melanoma auxiliary diagnosis classification model constructed by this algorithm can be improved by about 1%,which has certain use value.

The sequence of a gene determines its structure,and the structure of a gene reflects biological functional traits.Therefore,scientific data visualization of viral gene sequences has become one of the widely used methods.There is an increasing demand for visual manipulation of biological gene sequences.Therefore,based on the most advanced bioinformatics analysis and hierarchical structured bioinformatics knowledge model,the method of multiple probability measures is proposed to statistically analyze the bluetongue virus gene sequence,and combined with computer visualization methods,the characteristics of the bluetongue virus under different projections are presented.Compared with traditional virus research methods,this method is intuitive and concise,and it is easy to use.This method provides rich visualization under different measurement coordinates,reflecting the classification characteristics of bluetongue virus.Results generated by this method are compared with the phylogenetic tree generated by traditional biological analysis methods,which could provide references for homology analysis and the study of the evolutionary relationship of bluetongue virus.It is conducive to in-depth study of bluetongue virus from various angles.

The growth of electronic medical records forms the basis of user health big data,which can improve the quality of medi-cal services and reduce medical costs.Therefore,the rapid and effective retrieval of cases has practical significance in clinical medi-cine.Electronic medical records have strong professionalism and unique text characteristics.However,traditional text retrieval methods have the disadvantages of inaccurate text entity semantic expression and low retrieval accuracy.In view of the above characteristics and problems,this paper proposes a fusion BERT-BiLSTM model structure to fully express the semantic information of the electronic medical record text and improve the accuracy of retrieval.This research is based on public data.First,correlation extension retrieval keywords prerpocessing is carried on the open standard Chinese EMR data according to clinical diagnosis rules.Secondly,the BERT model is used to dynamically obtain the word granularity vector matrix according to the context of the medical record text,then the generated word vector is used as the input of the bidirectional long and short-term memory network model(BiLSTM) to extract the global semantic features of the context information.Finally,the feature vector of the retrieved document is mapped to the Euclidean space,and the medical record text closest to the retrieved document is found to realize the text retrieval of unstructured clinical data.Simulation results show that this method can dig out multi-level and multi-angle text semantic features from the medical record text,the F1 value obtained on the electronic medical record data set is 0.94,which can significantly improve the accuracy of text semantic retrieval.

The information construction of hospitals has entered the intelligent era,more and more medical cyber physical systems(CPS) have been applied in hospitals.However,in the complication disease treatment scenario,medical CPS are not reliable enough because of the specialization of medical disciplines and the lack of medical knowledge base.In this paper,a collaborative architecture of medical CPS is proposed to improve the decision reliability of medical CPS.On the collaboration platform,CPS send cooperative tasks to intelligent units on the network,and the intelligent units assist CPS to make medical decisions together.In this paper,the control strategy of the cooperative network is optimized to improve the network communication efficiency because the physiological data of patients are continuous dynamic data and medical CPS have a high requirement on the timeliness of response.CCD and HCD algorithms are proposed respectively for the deployment of high-level controller and low-level controller.Finally,two algorithms are simulated and compared with K-means algorithm.The results show that HCD algorithm greatly improves the load balancing of low-level controllers at the expense of less average communication delay.CCD algorithm is more suita-ble for advanced controller deployment with fewer cluster nodes,and its optimization effect on objective function is obviously better than that of HCD algorithm and K-means algorithm.

Pleural effusion is a complication of many major diseases.Invasive puncture and biochemical tests are the gold standard for diagnoising the property of pleural effusion.Therefore,a non-invasive pleural effusion analysis method is of great significance.A model based on standardization of pleural effusion ultrasonic image—Property analysis method of pleural effusion(PAMPE) is proposed.PAMPE can quickly and noninvasively classify three laboratory indexes:effusion color,effusion turbidity and Rivalta test.The construction of PAMPE is mainly divided into three steps:image standardization,construction of feature engineering and using v-SVM to build PAMPE after feature selection.In the image standardization step,a new standardization method—Standardi-zation of Pleural Effusion Ultrasonic Image(SOPEU) is also proposed.SOPEU suppresses the differences in the grayscale and scale of the images in the image set caused by the different parameters of ultrasound equipment,the different degree of obesity of patients,and the different degree to which pleural effusion is shielded by the bones and diaphragm.Experiment results illustrate that,PAMPE behaves well in a variety of evaluation indicators:accuracy,precision,recall,F1-score,confusion matrix,receiver operating characteristic(ROC) curve and area under ROC curve(AUC).Specifically,for the three classification problems,the accuracy can reach 0.800,0.743 and 0.719,the precision can reach 0.806,0.779 and 0.741,the recall can reach 0.921,0.815 and 0.893,the F1-score can reach 0.860,0.796 and 0.809 and the AUC can reach 0.820,0.700 and 0.709,which proves the effectiveness of PAMPE from different aspects.Comparative results shows that for the three classification problems,PAMPE has increased the accuracy of 0.090,0.048 and 0.086 respectively compared with the model constructed without SOPEU.The experimental results show that the normalized images effectively reduce the classification errors caused by the different quality of data sources.

With air pollution getting worse and worse,lung cancer has become one of the malignant tumors with the fastest increasing morbidity and mortality rate,which seriously endangers people's life and health.The early stage of lung cancer is mainly in the form of pulmonary nodules.If the early stage of lung cancer can be detected and treated in time,the treatment effect of lung cancer will be improved.Low-dose spiral CT is widely used in the diagnosis of pulmonary nodules because of its characteristics of fast acquisition speed,low cost and low radiation.At present,CT image diagnosis mostly adopts the traditional manual diagnosis and CAD system diagnosis,but these two methods have the disadvantages of low accuracy and poor generalization.In view of the above problems,this paper takes the detection of pulmonary nodules in the field of medical assisted diagnosis as the research object,and proposes an improved low-dose CT image automatic detection algorithm for pulmonary nodules based on CNN.Firstly,the CT images are preprocessed to extract the lung parenchyma.Secondly,the cascade-rcnn candidate nodule screening network is improved to extract higher quality targets.Thirdly,an improved 3D CNN false positive reduction network is proposed to improve the accuracy of nodular classification.Finally,experiments are carried out on Luna16 dataset.Compared with existing algorithms,the detection accuracy of the proposed algorithm is improved.

In recent years,using deep learning to classify Alzheimer's disease has become one of the hotspots in medical image research.But the existing models are difficult to extract medical image features effectively,what's more,the auxiliary information resources of disease classification are wasted.To solve these problems,a classification method of Alzheimer's disease with attention mechanism and multi-task learning based on the deep 3D convolution neural network is proposed.Firstly,using the improved traditional C3D network,a rough low-level feature map is generated.Secondly,this feature map is input into a convolution block with attention mechanism and a common convolution block respectively.The former focuses on the structural characteristics of MRI images,and can obtain the attention weight of different pixel in the feature map,which is multiplied by the output feature map of the latter.Finally,multi-task learning is used to obtain three kinds of outputs by adding different full connected layer.The other two outputs optimize the main classification task through back propagation in the training process.Experimental results show that,compared with the existing classification methods of Alzheimer's disease,the classification accuracy and other indicators of the proposed method on ADNI data set have been improved,which is helpful to promote the follow-up disease classification research.