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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Purpose The purpose of this paper is to identify the negative impact of an incumbent supplier pushing out a buyer, the positive effect of an alternative supplier pulling a buyer, and the mooring impact that prevents a buyer from switching to a supplier in terms of the push-pull-mooring (PPM) model of migration theory. In this context, this study considers a buyer as the immigrant, an incumbent supplier as the origin, an alternative supplier as the destination, and inertia as the hesitance to migrate. Design/methodology/approach This study collected survey data from 148 end-product manufacturers and first-tier suppliers. It tested whether the PPM model fit in a supply chain relationship (SCR) using the partial least squares structural equation modelling approach and SmartPLS package version 2.0.M3. Findings The results support all hypotheses for causal relationships among factors of cognitive, affect, and behavioural intentions of each PPM effect. This study identifies the relative importance of each effect on a buyer’s intention of switching an existing supplier. Originality/value This study presents a new perspective that enhances the understanding of a buyer’s behaviour towards a supplier by applying the PPM model of migration to a manufacturing SCR. It promotes interdisciplinary and integrated views as well as broadens the diversity of the results in the business-to-business context.

Rationale:Recent studies have used diffusion tensor tractography (DTT) to demonstrate that central poststroke pain (CPSP) was related to spinothalamic tract (STT) injury in patients with stroke. However, few studies have been reported about delayed-onset CPSP due to degeneration of the STT following a stroke.Patient's concerns:A 57-year-old female patient presented with right hemiparesis after stroke. Two weeks after onset, she did not report any pain. At approximately 6 months after onset, she reported pain in the right arm and leg, and the pain slowly intensified with the passage of time. At 14 months after onset, the characteristics and severity of her pain were assessed to be continuous pain without allodynia or hyperalgesia; tingling and cold-sensational pain in her right whole arm and leg (visual analog scale score: 5).Diagnoses:The patient was diagnosed as the right hemiparesis due to spontaneous thalamic hemorrhage.Interventions:Clinical assessment and diffusion tensor imaging (DTI) were performed 2 weeks and 14 months after onset.Outcomes:She suffered continuous pain in her right whole arm and leg (visual analog scale score: 5). On DTT of the 2-week postonset DTI scans, the configuration of the STT was well-preserved in both hemispheres. However, in contrast to those 2-week postonset results, the 14-month postonset DTT results showed partial tearing and thinning in the left STT. Regardless, both the 2-week and 14-month postonset DTT showed that the left STT passed through the vicinity of the thalamic lesion.Lessons:Diagnostic importance of performing a DTT-based evaluation of the STT in patients exhibiting delayed-onset CPSP following intracerebral hemorrhage.

As previously protected and emerging markets continue to open up for international trade, export firms often have a difficult time developing marketing strategies, particularly pricing strategies. However, few studies focus on the pricing practices of export firms, which makes it difficult to understand whether the same pricing strategies apply across markets, particularly in emerging markets. Using a framework of price complexity, the authors examine and compare the cost variables that are factored into price (price complexity) by export firms in the United States and Korea. The authors also investigate and compare some important noncost factors that influence pricing decisions for exporters in both their domestic and international markets. The results show that firms from the United States, a developed market, tend to factor more cost variables into price than do firms from an emerging market such as Korea. On the basis of the results of the study, the authors discuss implications for exporters and future research directions.