Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field.

View this article on IEEE Xplore

Text Detection and Recognition for Images of Medical Laboratory Reports With a Deep Learning Approach

 

The adoption of electronic health records (EHRs) is an important step in the development of modern medicine. However, complete health records are not often available during treatment because of the functional problem of the EHR system or information barriers. This paper presents a deep-learning-based approach for textual information extraction from images of medical laboratory reports, which may help physicians solve the data-sharing problem. The approach consists of two modules: text detection and recognition. In text detection, a patch-based training strategy is applied, which can achieve the recall of 99.5% in the experiments. For text recognition, a concatenation structure is designed to combine the features from both shallow and deep layers in neural networks. The experimental results demonstrate that the text recognizer in our approach can improve the accuracy of multi-lingual text recognition. The approach will be beneficial for integrating historical health records and engaging patients in their own health care.

View this article on IEEE Xplore