Aiming at the problem that the pre-training model BERT （Bidirectional Encoder Representation from Transformers） lacks of vocabulary information， a Chinese named entity recognition model called OpenKG + Entity Enhanced BERT + CRF （Conditional Random Field） based on knowledge base entity enhanced BERT model was proposed on the basis of the semi-supervised entity enhanced minimum mean-square error pre-training model. Firstly， documents were downloaded from Chinese general encyclopedia knowledge base CN-DBPedia and entities were extracted by Jieba Chinese text segmentation to expand entity dictionary. Then， the entities in the dictionary were embedded into BERT for pre-training. And the word vectors obtained from the training were input into Bidirectional Long-Short-Term Memory network （BiLSTM） for feature extraction. Finally， the results were corrected by CRF and output. Model validation was performed on datasets CLUENER 2020 and MSRA， and the proposed model was compared with Entity Enhanced BERT pre-training， BERT+BiLSTM， ERNIE and BiLSTM+CRF models. Experimental results show that compared with these four models， the proposed model has the F1 score increased by 1.63 percentage points and 1.1 percentage points， 3.93 percentage points and 5.35 percentage points， 2.42 percentage points and 4.63 percentage points， 6.79 and 7.55 percentage points， respectively in the two datasets. It can be seen that the comprehensive effect of the proposed model on named entity recognition is effectively improved， and the F1 scores of the model are better than those of the comparison models.

Ultrasound imaging is widely used in clinical diagnosis because of its advantages of convenience， low cost and non-radiation， however， speckle noise in the image may adversely affect clinical diagnosis or subsequent image analysis.As a typical denoising technology， when using Non-Local Means Filter（NLMF）for speckle removal of ultrasonic image，there will be shortcomings such as high time consumption and difficulty in setting filtering parameters. Therefore， a Multi-scale Fast Non-Local Means Filter （MF-NLMF） algorithm was proposed to remove speckle noise of ultrasonic image. A Fast NLMF （F-NLMF） algorithm was first give out to reduce the computing time by using the mutual correlation filtering technique. Then multiple window parameters were set to obtain multiple speckle removal results， and the model parameters were able to be adjusted adaptively according to the window size. The final speckle removal image was obtained by fusing the multiple speckle removal results. Experimental results show that under the same experimental conditions， the F-NLMF algorithm reduces the computing time by at least 96.04% compared with the traditional NLMF algorithm. Compared with other six algorithms such as Iterative Bayesian Non-Local Mean Filtering （IBNLMF）， the proposed MF-NLMF has the speckle removal image with the Peak Signal-to-Noise Ratio （PSNR） value improved by more than 0.73 dB， the Feature SIMilarity index （FSIM） value increased by more than 0.011， the Contrast-to-Noise Ratio （CNR） and Signal-to-Noise Ratio （SNR） values raised by more than 0.000 5 and 0.001 6 respectively.

IntraVascular UltraSound (IVUS) imaging can provide information of the coronary atherosclerotic plaque. It allows the doctor to make comprehensive and accurate evaluation of diseased vessel. Some ultrasound data collecting devices for imaging system exhibited insufficient data transfer speed, high cost or inflexibility, so the authors presented a high speed data transfer and imaging method for intravascular ultrasound. After being collected and processed, ultrasound data was transferred to computer through USB3.0 interface. In addition, logarithmic compression and digital coordinate conversion were applied in computer before imaging. Data transmission experiment shows that the transfer speed always stays around 2040Mb/s. Finally, phantom imaging was conducted to demonstrate the performance of the system. It shows a clear pipe wall and a smooth luminal border.