Deep Multi-Objective Learning from Low-Dose CT for Automatic Lung-RADS Report Generation.

Radiology report generation through chest radiography interpretation is a time-consuming task that involves the interpretation of images by expert radiologists. It is common for fatigue-induced diagnostic error to occur, and especially difficult in areas of the world where radiologists are not available or lack diagnostic expertise. In this research, we proposed a multi-objective deep learning model called CT2Rep (Computed Tomography to Report) for generating lung radiology reports by extracting semantic features from lung CT scans. A total of 458 CT scans were used in this research, from which 107 radiomics features and 6 slices of segmentation related nodule features were extracted for the input of our model. The CT2Rep can simultaneously predict position, margin, and texture, which are three important indicators of lung cancer, and achieves remarkable performance with an F1-score of 87.29%. We conducted a satisfaction survey for estimating the practicality of CT2Rep, and the results show that 95% of the reports received satisfactory ratings. The results demonstrate the great potential in this model for the production of robust and reliable quantitative lung diagnosis reports. Medical personnel can obtain important indicators simply by providing the lung CT scan to the system, which can bring about the widespread application of the proposed framework.

Refined distributed emotion vector representation for social media sentiment analysis.

As user-generated content increasingly proliferates through social networking sites, our lives are bombarded with ever more information, which has in turn has inspired the rapid evolution of new technologies and tools to process these vast amounts of data. Semantic and sentiment analysis of these social multimedia have become key research topics in many areas in society, e.g., in shopping malls to help policymakers predict market trends and discover potential customers. In this light, this study proposes a novel method to analyze the emotional aspects of Chinese vocabulary and then to assess the mass comments of the movie reviews. The experiment results show that our method 1. can improve the machine learning model by providing more refined emotional information to enhance the effectiveness of movie recommendation systems, and 2. performs significantly better than the other commonly used methods of emotional analysis.