Layout-aware Subfigure Decomposition for Complex Figures in the Biomedical Literature

Published scientific figure is a valuable information resource, but often occur as composite images. The ImageCLEF meeting presented a shared evaluation in 2016 to use machine learning to split these composite figures into components automatically. We adapted an existing high-performance object detection method to analyze the substructure of published biomedical figures by developing a novel multi-branch output convolution neural network to predict irregular panel layouts and provide augmented training data to drive learning. Our system has an accuracy of 86.8% on the 2016 ImageCLEF Medical dataset and 83.1% on a new dataset derived from open access papers from the INTACT database of molecular interactions.