Viktor Gál, Illés Solt, Etienne Kerre, and Mike Nachtegael
sparse coding, medical image processing
Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, flowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as figures taken from the millions of scientific publications. We present a multi-disciplinary approach to tackle the classification problem by combining image features, meta-data, textual and referential information. We test our system’s accuracy on the ImageCLEF 2011 medical modality classification data set. We show that using sparse cod- ing for the Bag-of-Words image representation and Sparse Logistic Regression on the extracted features significantly increases accuracy. The best method achieves 87.3% accu- racy and outperforms the state of the art.
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