Analysis of the factors contributing to endometriosis in China and UK / 南方医科大学学报

OBJECTIVE@#To explore the differences in the factors associated with endometriosis between Chinese and British patients.@*METHODS@#This case-control study was conducted in 387 patients with endometriosis and 199 non-endometriosis patients admitted to John Radcliffe Hospital (Oxford, UK) and in 101 patients with endometriosis and 50 non-endometriosis patients admitted in the First Affiliated Hospital of Guangzhou University of Chinese Medicine. The clinical data including height, weight, body mass index, marital status, employment, menstruation, fertility, and operation reasons were collected via a standardized WERF EPHect questionnaire.@*RESULTS@#Multivariate logistic regression analysis indicated that body mass index, surgery for dysmenorrhea, history of pregnancy, counts of previous surgeries for endometriosis and status of employment were all significantly associated with endometriosis in the UK (P < 0.05), while a history of dysmenorrhea was significantly correlated with endometriosis in Chinese patients (P < 0.05).@*CONCLUSION@#Dysmenorrhea may be the most important common factor associated with endometriosis in China and the UK, but the other factors contributing to endometriosis may differ between these two countries.

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection.

As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.

A new deep learning model for assisted diagnosis on electrocardiogram.

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on electrocardiogram for clinical medical service. It combines two sub networks which are convolutional neural network (CNN) and bi-directional recurrent neural network (BRNN). In the model, CNN with one-dimension convolution is employed to extract features for each lead of ECG, and BRNN is used to fuse features of different leads to represent deeper features. In the training step, we use more than 40 thousand training data and more than 19 thousand validation data to obtain the optimal parameters of the model. Besides, by validating our model on more than CCDD 120,000 real data, it achieves an 87.69% accuracy rate, higher than popular deep learning models such as CNN and ResNet. Our model has better accuracy than state-of-the-art models and it is also slightly higher than the average accuracy of human judgement. It can be served for the first round screening of ECG examination clinical diagnosis.

A sparse deep learning model for privacy attack on remote sensing images.

Deep learning tools have been a new way for privacy attacks on remote sensing images. However, since labeled data of privacy objects in remote sensing images are less, the samples for training are commonly small. Besides, traditional deep neural networks have a huge amount of parameters which leads to over complexity of models and have a great heavy of computation. They are not suitable for small sample image classification task. A sparse method for deep neural network is proposed to reduce the complexity of deep learning model with small samples. A singular value decomposition algorithm is employed to reduce the dimensions of the output feature map of the upper convolution layers, which can alleviate the input burden of the current convolution layer, and decrease a large number of parameters of the deep neural networks, and then restrain the number of redundant or similar feature maps generated by the over-complete schemes in deep learning. Experiments with two remote sensing image data sets UCMLU and WHURS show that the image classification accuracy with our sparse model is better than the plain model,which is improving the accuracy by 3%,besides, its convergence speed is faster.

Research on and practice in the framework of hospital performance management systems / 中华医院管理杂志

The paper first discusses the concept of hospital performance management systems and their relationship with the business systems and then dwells on the framework of the performance management systems, including extended business systems, database systems of shared business, subject driven systems and commercial intelligence systems. It also gives an account of the results of actual application of the Hospital Operational Performance Management Systems developed by the hospital the authors work with.