Thoracic Aortic Aneurysm and Dissection (TAAD) is one of the life-threatening cardiovascular diseases, and the histological changes of Medial Degeneration (MD) have important clinical significance for the diagnosis and early intervention of TAAD. Focusing on the issue that the diagnosis of MD is time-consuming and prone to poor consistency because of the great complexity in histological images, a deep learning based classification method of histological images was proposed, and it was applied to four types of MD pathological changes to verify its performance. In the method, an improved Convolutional Neural Network (CNN) model was employed based on the GoogLeNet. Firstly, transfer learning was adopted for applying the prior knowledge to the expression of TAAD histopathological images. Then, Focal loss and L2 regularization were utilized to solve the data imbalance problem, so as to optimize the model performance. Experimental results show that the proposed model is able to achieve the average accuracy of four-class classification of 98.78%, showing a good generalizability. It can be seen that the proposed method can effectively improve the diagnostic efficiency of pathologists.

For the typical steganographic algorithms in JPEG images, this paper firstly analyzed the correlation between neighboring coefficients of intra- and inter-block in Discrete Cosiine Transform (DCT) domain, and then extracted the conditional distribution probability matrix of the bilateral coefficients as the sensitive steganalytic features by taking the middle coefficient of three neighboring coefficients as the condition. At last, this paper proposed a JPEG image steganalytic algorithm on a basis of bilateral transition probability distribution of DCT coefficients. The experimental results show that, for different embedding ratios, the algorithm proposed in this paper outperforms the existing algorithms.