Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images.

Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.

Differences in connectivity patterns between child and adolescent attention deficit hyperactivity disorder patients.

Attention deficit hyperactivity disorder (ADHD) is a common psychological disorder for a broad range of ages. Child and adolescent ADHD patients show different behavior patterns. The differences between child and adolescent ADHD patients have not been fully explored in terms of brain connectivity. In this study, we explored the differences of connectivity patterns between child and adolescent ADHD patients using resting-state functional magnetic resonance imaging (rs-fMRI) of 52 ADHD patients (26 children and 26 adolescents). Default mode network and frontoparietal network showed significant group-wise connectivity pattern differences between child and adolescent ADHD patients. The results of our study might suggest potential imaging biomarkers for further ADHD related studies.