Most of the current Chinese questions and answers matching technologies require word segmentation first， and the word segmentation problem of Chinese medical text requires maintenance of medical dictionaries to reduce the impact of segmentation errors on subsequent tasks. However， maintaining dictionaries requires a lot of manpower and knowledge， making word segmentation problem always be a great challenge. At the same time， the existing Chinese medical questions and answers matching methods all model the questions and the answers separately， and do not consider the relationship between the keywords contained in the questions and the answers respectively. Therefore， an Attention mechanism based Stack Convolutional Neural Network （Att-StackCNN） model was proposed to solve the problem of Chinese medical questions and answers matching. Firstly， character embedding was used to encode the questions and answers to obtain the respective character embedding matrices. Then， the respective feature attention mapping matrices were obtained by constructing the attention matrix using the character embedding matrices of the questions and answers. After that， Stack Convolutional Neural Network （Stack-CNN） model was used to perform convolution operation to the above matrices at the same time to obtain the respective semantic representations of the questions and answers. Finally， the similarity was calculated， and the max-margin loss was calculated by using the similarity to update the network parameters. On the cMedQA dataset， the Top-1 accuracy of proposed model was about 1 percentage point higher than that of Stack-CNN model and about 0.5 percentage point higher than that of Multi-CNNs model. Experimental results show that Att-StackCNN model can improve the matching effect of Chinese medical questions and answers.

Brain tumor Magnetic Resonance Imaging(MRI) has problems such as multi-modality, lacking of training data, class imbalance, and large differences between private databases, which lead to difficulties in segmentation. In order to solve these problems, the few-shot segmentation method was introduced, and a Prototype network based on U-net(PU-net) was proposed to segment brain tumor Magnetic Resonance(MR) images. First, the U-net structure was modified to extract the features of various tumors, which was used to calculate the prototypes. Then, on the basis of the prototype network, the prototypes were used to classify the spatial locations pixel by pixel, so as to obtain the probability maps and segmentation results of various tumor regions. Aiming at the problem of class imbalance, the adaptive weighted cross-entropy loss function was used to reduce the influence of the background class on loss calculation. Finally, the prototype verification mechanism was added, which means the probability maps obtained by segmentation were fused with the query image to verify the prototypes. The proposed method was tested on the public dataset BraTS2018, and the obtained results were as following:the average Dice coefficient of 0.654, the positive prediction rate of 0.662, the sensitivity of 0.687, the Hausdorff distance of 3.858, and the mean Intersection Over Union(mIOU) reached 61.4%. Compared with Prototype Alignment Network(PANet) and Attention-based Multi-Context Guiding Network(A-MCG), all indicators of the proposed method were improved. The results show that the introduction of the few-shot segmentation method has a good effect on brain tumor MR image segmentation, and the adaptive weighted cross-entropy loss function is also helpful, which can play an effective auxiliary role in the diagnosis and treatment of brain tumors.