Convolutional Neural Networks for Subfigure Classification.
David Lyndon, Ashnil Kumar, Jinman Kim, Philip H. W. Leong, Dagan D. Feng · CLEF (Working Notes) · 2015
A major challenge for Medical Image Retrieval (MIR) is the discovery of relationships between low-level image features (intensity, gradient, texture, etc.) and high-level semantics such as modality, anatomy or pathology. Convolutional Neural Networks (CNNs) have been shown to have an inherent ability to automatically extract hierarchical representations from raw data. Their successful application in a variety of generalised imaging tasks suggests great potential for MIR. However, a major hurdle to their deployment in the medical domain is the relative lack of robust training corpora when compared to general imaging benchmarks such as ImageNET and CIFAR. In this paper, we present the adaptation of CNNs to the subfigure classification subtask of the medical classification task at ImageCLEF 2015.