Optimized convolutional neural network ensembles for medical subfigure classification

Abstract

Automatic classification systems are required to support medical literature databases like PubMedCentral, which allow an easy access to millions of articles. FHDO Biomedical Computer Science Group (BCSG) participated at the ImageCLEF 2016 Subfigure Classification Task to improve existing approaches for classifying figures from medical literature. In this work, a data analysis is conducted in order to improve image preprocessing for deep learning approaches. Evaluations on the dataset show better ensemble classification accuracies using only visual information with an optimized training, in comparison to the mixed feature approaches of BCSG at ImageCLEF 2016. Additionally, a self-training approach is investigated to generate more labeled data in the medical domain.