Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.

BackgroundThe concept of cognitive insight refers to the cognitive processes involved in patients’ re-evaluation of their anomalous experiences and of their misinterpretations. The purpose of the present study was to examine the relationship between cognitive insight and subjective quality of life in patients with schizophrenia to further shed light on the nature of cognitive insight and its functional correlates in schizophrenia.MethodsSeventy-one stable outpatients with schizophrenia were evaluated for cognitive insight and subjective quality of life using the Beck Cognitive Insight Scale (BCIS) and the Schizophrenia Quality of Life Scale Revision 4 (SQLS-R4). The symptoms of schizophrenia were also assessed. Pearson’s correlation analysis and partial correlation analysis that controlled for the severity of symptoms were performed to adjust for the possible effects of symptoms.ResultsThe self-reflectiveness subscale score of the BCIS had significant positive correlations with the SQLS-R4 psychosocial domain and total SQLS-R4 scores, indicating that the higher the level of cognitive insight, the lower the subjective quality of life. In partial correlation analysis controlling for symptoms, the BCIS self-reflectiveness subscale score still had a significant correlation with the SQLS-R4 psychosocial domain score. The correlation coefficient between the BCIS self-reflectiveness and total SQLS-R4 scores was reduced to a nonsignificant statistical tendency.ConclusionThe results of our study suggest that cognitive insight, particularly the level of self-reflectiveness, is negatively associated with the level of subjective quality of life in outpatients with schizophrenia and that this relationship is not wholly due to the confounding effect of symptoms. Future studies are necessary to explore possible mediating and moderating factors and to evaluate the effects of therapeutic interventions on the relationship.

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