Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep conventional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully-sampled image. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care

Homogeneity and structure identification in semiparametric factor models

10.1080/07350015.2020.1831516Journal of Business & Economic Statistics401408-42

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the “Tree Hole”. The purpose of this article is to support the “Tree Hole” rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of “Tree Hole” named “Zou Fan”, this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user’s message in different time dimensions. Through detailed investigation of the research results, we found that the number of “Tree Hole” messages in multiple time dimensions is positively correlated to emotion. The longer the “Tree Hole” is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of “Tree Hole” rescue, volunteers should focus on the long-formed “Tree Hole” and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events

Home Self-medication Question-Answering System for the Elderly Based on Seq2Seq Model and Knowledge Graph Technology

With the deepening of aging, chronic diseases of the elderly are the main burden of disease in most countries in the world. The prevalence of chronic diseases in urban areas in China is as high as 75%. Many elderly people use multiple drugs for a long time. Home self-medication problems occur frequently. In order to alleviate this problem to a certain extent, knowledge graph technology and a deep learning model are used to design a home self-medication question-answering system for the elderly and their caregivers. Explore a feasible way of providing automated online consultation intelligent services. In this paper, we have collected medication as well as professional Q&amp;A (question and answer) data in the field of aging health, and constructed a knowledge graph that meets the characteristics of medication use in the elderly. Based on the matching rules in the question judging module, the problems entered by users are classified. For professional knowledge related to diseases and medications of the elderly, the question-answering system uses the knowledge graph to search for answers. For other basic knowledge related to elderly health, the system uses the BERT model to vectorize its users’ questions, then matches the questions by calculating cosine similarity, thus finding the corresponding answers. The system adds the Seq2Seq model as a supplement to the answer retrieval method of the knowledge graph. The testing results shows that the system provides online consultation services more accurately and efficiently for home self-medication for the elderly and their caregivers.</p

Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology

With the development and progress of society, people are facing increasing pressure. The emergence of this phenomenon has led to a rapid increase in the incidence of mental illness. In order to deal with this phenomenon, this paper proposes a system of question and answering on the basic knowledge of mental health (MHQ&A) by using deep learning retrieval technology and knowledge graph technology. The system MHQ&A is designed mainly for the general public, to answer the basic knowledge of mental health, especially the field of depression. First of all, the basic and the professional question and answer data about mental health were respectively obtained by the reptilian bot from the "IASK"website knowledge and the "Dr. Dingxiang"website. Then, the questions and answers obtained through the crawler are made into a Question and Answering Knowledge Graph of Basic Health Knowledge in the mental health field, which is combined with semantic data of antidepressants and the semantic data of depression papers. Finally, a set of template matching rules is designed to determine the type of problem of users. If the questions are about the professional knowledge of medicine or thesis, the reasoning template will be used to reason and search the answer in the "Question and Answering Knowledge Graph of Basic Health Knowledge in the Mental Health Field". If the questions are about other basic knowledge in the field of mental health, the BERT model is used to vectorize the questions of users, and the matching questions and corresponding answers in the MHQ&A are found through cosine similarity calculation. Through the test of system accuracy, it is proved that the system can effectively combine deep learning technology and knowledge