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In clinical medicine, the contrast-enhanced ultrasound(CEUS) has been a commonly used imaging modality for diagnosis of breast tumor. However, most researchers in computer vision field only focus on B-mode ultrasound image which does not get good results. To improve the accuracy of classification, first, we propose a novel method, i.e., a Temporal Sequence Dual-Branch Network(TSDBN) which, for the first time, can use B-mode ultrasound data and CEUS data simultaneously. Second, we designed a new Gram matrix to model the temporal sequence, and then proposed a Temporal Sequence Regression Mechanism (TSRM), which is a novel method to extract the enhancement features from CEUS video based on the matrix. For B-mode ultrasound branch, we use the traditional ResNeXt network for feature extraction. While CEUS branch uses ResNeXt + R(2 + 1)D network as the backbone network. We propose a TSRM to learning temporal sequence relationship among frames, and design a Shuffle Temporal Sequence Mechanism(STSM) to shuffle temporal sequences, the purpose of which is to further enhance temporal information among frames. Experimental results show that the proposed TSRM could use temporal information effectively and the accuracy of TSDBN is higher than that of state-of-art approaches in breast cancer classification by nearly 4%. INDEX TERMS Breast cancer classification, temporal sequence, contrast-enhanced ultrasound (CEUS), shuffle mechanism. WENBIN LIU received the B.S. degree in communication engineering from Southwest Jiaotong University, in 2005, and the master's degree in communication and information system from the

Objects This study aims to explore the Cancer antigen 724 (CA724) reference values spatial distribution characteristics in healthy Chinese adults. The study can provide regional reference for medical diagnosis. Study Design The relationship between CA724 and 25 geographical environmental factors was analyzed firstly. Artificial neural network simulation training was used to construct the prediction model. The national forecast distribution map of the CA724 reference values was obtained by the geostatistical mapping method. Analyzing and exploring the influence mechanism of geographical environment factors on CA724 reference values. Methods Collecting 34470 cases from more than 106 cities healthy adults CA724 reference values via several paper databases in 10 recent years. Correlation analysis, RBF artificial neural networks and trend surface analysis were applied to explore if there was any tendency of spatial variation. The Kriging interpolation of geostatistical analysis was developed to reveal the spatial distribution characteristics of the CA724 reference values. Results The distribution of CA724 reference values of Chinese healthy adults shows a downward trend from south to north. CA724 reference values have negative correlations with latitude, annual sunshine duration and topsoil cation exchange capacity in clay. CA724 have positive correlations with annual mean air temperature, annual mean relative humidity, and annual precipitation amount. High temperature and high humidity environment will reduce gastrointestinal function and breeze various mold bacteria. Lack of sunshine can easily lead to vitamin C deficiency in the body. These will increase the incidence of gastrointestinal diseases and gastric cancer, then increase the CA724 value. Conclusion CA724 reference values show spatial autocorrelation and regional variation. There are some geographical environment factors effected Chinese healthy adults CA724 reference values. Geographic factors such as sunshine, temperature, and humidity have effects on CA724 reference values can provide new ideas and directions of prevention and clinical diagnosis in the future.