The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data.Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurate...The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data.Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurately.But because of the particularity of medical images,traditional contentbased image retrieval(CBIR)method such as bag-of-words(BOW)cannot be applied to medical images.For example,when retrieving a diseased image,we should not only consider the similar characteristics but also need to consider the type of lesion.And for medical images,images with the same lesion may have different image features,similar images may have different types of lesions.In this paper,a Markov random field(MRF)is structured,and an approximate belief propagation algorithm is used to retrieval images.An adjust-ranking step after initial retrieval is incorporated to further improve the retrieval performance.This paper uses the real brain CT images.The experimental results show that the proposed method can significantly improve the retrieval accuracy and has good efficiency.展开更多
Medical images are important for medical research and clinical diagnosis.The research of medical images includes image acquisition,processing,analysis and other related research fields.Crowdsourcing is attracting grow...Medical images are important for medical research and clinical diagnosis.The research of medical images includes image acquisition,processing,analysis and other related research fields.Crowdsourcing is attracting growing interests in recent years as an effective tool.It can harness human intelligence to solve problems that computers cannot perform well,such as sentiment analysis and image recognition.Crowdsourcing can achieve higher accuracies in medical image classification,but it cannot be widely used for its low efficiency and the monetary cost.We adopt a hybrid approach which combines computer’s algorithm and crowdsourcing system for image classification.Medical image classification algorithms have a high error rate near the threshold.And it is not significant by improving these classification algorithms to achieve a higher accuracy.To address the problem,we propose a hybrid framework,which can achieve a higher accuracy significantly than only use classification algorithms.At the same time,it only processes the images that classification algorithms perform not well,so it has a lower monetary cost.In the framework,we device an effective algorithm to generate a range-threshold that assign images to crowdsourcing or classification algorithm.Experimental results show that our method can improve the accuracy of medical images classification and reduce the crowdsourcing monetary cost.展开更多
The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series ...The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series prediction models,including RNN,LSTM and their deformed bodies,show the problems of gradient disappearance and low efficiency in long-span prediction.This paper proposes a long-term and short-term memory network architecture,which based on Encoder and Decoder Stacks and self-attention mechanism,replacing the feature extraction part of traditionalLSTMthrough self-attentionmechanism and provides interpretable insights into the dynamics of time.Through the results of simulation experiments,this paper shows the comparison of stock prediction effects through using RNN,Bi-LSTM and Encoder and Decoder-Attention-LSTM models.The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model,and can achieve high accuracy when the epoch is small.展开更多
文摘The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data.Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurately.But because of the particularity of medical images,traditional contentbased image retrieval(CBIR)method such as bag-of-words(BOW)cannot be applied to medical images.For example,when retrieving a diseased image,we should not only consider the similar characteristics but also need to consider the type of lesion.And for medical images,images with the same lesion may have different image features,similar images may have different types of lesions.In this paper,a Markov random field(MRF)is structured,and an approximate belief propagation algorithm is used to retrieval images.An adjust-ranking step after initial retrieval is incorporated to further improve the retrieval performance.This paper uses the real brain CT images.The experimental results show that the proposed method can significantly improve the retrieval accuracy and has good efficiency.
文摘Medical images are important for medical research and clinical diagnosis.The research of medical images includes image acquisition,processing,analysis and other related research fields.Crowdsourcing is attracting growing interests in recent years as an effective tool.It can harness human intelligence to solve problems that computers cannot perform well,such as sentiment analysis and image recognition.Crowdsourcing can achieve higher accuracies in medical image classification,but it cannot be widely used for its low efficiency and the monetary cost.We adopt a hybrid approach which combines computer’s algorithm and crowdsourcing system for image classification.Medical image classification algorithms have a high error rate near the threshold.And it is not significant by improving these classification algorithms to achieve a higher accuracy.To address the problem,we propose a hybrid framework,which can achieve a higher accuracy significantly than only use classification algorithms.At the same time,it only processes the images that classification algorithms perform not well,so it has a lower monetary cost.In the framework,we device an effective algorithm to generate a range-threshold that assign images to crowdsourcing or classification algorithm.Experimental results show that our method can improve the accuracy of medical images classification and reduce the crowdsourcing monetary cost.
基金This work is supported by the National Nature Science Foundation of China through project 51979048.
文摘The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series prediction models,including RNN,LSTM and their deformed bodies,show the problems of gradient disappearance and low efficiency in long-span prediction.This paper proposes a long-term and short-term memory network architecture,which based on Encoder and Decoder Stacks and self-attention mechanism,replacing the feature extraction part of traditionalLSTMthrough self-attentionmechanism and provides interpretable insights into the dynamics of time.Through the results of simulation experiments,this paper shows the comparison of stock prediction effects through using RNN,Bi-LSTM and Encoder and Decoder-Attention-LSTM models.The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model,and can achieve high accuracy when the epoch is small.
