In order to solve the problem of insufficient available training data in the classification task of breast mass and calcification, a multi-view model based on secondary transfer learning was proposed combining with imaging characteristics of mammogram. Firstly, CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) was used to construct the breast local tissue section dataset for the pre-training of the backbone network, and the domain adaptation learning of the backbone network was completed, so the backbone network had the essential ability of capturing pathological features. Then, the backbone network was secondarily transferred to the multi-view model and was fine-tuned based on the dataset of Mianyang Central Hospital. At the same time, the number of positive samples in the training was increased by CBIS-DDSM to improve the generalization ability of the network. The experimental results show that the domain adaption learning and data augmentation strategy improves the performance criteria by 17% averagely and achieves 94% and 90% AUC (Area Under Curve) values for mass and calcification respectively.

There are two main problems about pneumothorax detection in X-ray images. The pneumothorax usually overlaps with tissues such as ribs and clavicles in X-ray images, easily causing missed diagnosis and the performance of the existing pneumothorax detection methods remain to be improved. The suspicious pneumothorax area detection cannot be exploited by the convolutional neural network-based algorithms, lacking the interpretability. Aiming at the problems, a novel method combining Dense convolutional Network (DenseNet) and gradient-weighted class activation mapping was proposed. Firstly, a large-scale chest X-ray dataset named PX-ray was constructed for model training and testing. Secondly, the output node of the DenseNet was modified and a sigmoid function was added after the fully connected layer to classify the chest X-ray images. In the training process, the weight of cross entropy loss function was set to alleviate the problem of data imbalance and improve the accuracy of the model. Finally, the parameters of the last convolutional layer of the network and the corresponding gradients were extracted, and the areas of the pneumothorax type were roughly located by gradient-weighted class activation mapping. The experimental results show that, the proposed method has the detection accuracy of 95.45%, and has the indicators such as Area Under Curve (AUC), sensitivity, specificity all higher than 0.9, performs the classic algorithms of VGG19, GoogLeNet and ResNet, and realizes the visualization of pneumothorax area.

Concerning the problem that the traditional information leakage detection technology can not effectively detect implicit information leakage in Android applications, a reasoning method of Android Implicit Information Flow (ⅡF) combining control structure ontology model and Semantic Web Rule Language (SWRL) inference rule was proposed. Firstly, the key elements that generate implicit information flow in control structure were analyzed and modeled to establish the control structure ontology model. Secondly, based on the analysis of the main reasons of implicit information leakage, criterion rules of implicit information flow based on Strict Control Dependence (SCD) were given and converted into SWRL inference rules. Finally, control structure ontology instances and SWRL inference rules were imported into the inference engine Jess for reasoning. The experimental results show that the proposed method can deduce a variety of implicit information flow based on SCD with different nature and the testing accuracy of sample set is 83.3%, and the reasoning time is in the reasonable interval when the branch number is limited. The proposed model can effectively assist traditional information leakage detection to improve its accuracy.