Automated Risk Prediction for Esophageal Optical Endomicroscopic Images

Biomedical in vivo imaging has been playing an essential role in diagnoses and treatment in modern medicine. However, compared with the fast development of medical imaging systems, the medical imaging informatics, especially automated prediction, has not been fully explored. In our paper, we compared different feature extraction and classification methods for prediction pipeline to analyze in vivo endomicroscopic images, obtained from patients who are at risks for the development of gastric disease, esophageal adenocarcionoma. Extensive experiment results show that the selected feature representation and prediction algorithms achieved high accuracy in both binary and multi-class prediction tasks.