Modelling semantics from image data: opportunities from LIDC

While the advances in Computed Tomography (CT) technology allow better detection of pulmonary nodules by generating higher-resolution images, the new technology also generates more individual transverse reconstructions. As a result, the efficiency and accuracy of the radiologists interpreting these images is reduced. Double reading by two human observers has been shown to improve the detection of lung cancer. Given the increased cost of double reading and the variation among radiologists' interpretation, the objective is to develop computer-aided tools that could be used as 'second readers' when interpreting lung images by apriori rating the nodules based on automatically discovered image-semantics mappings.

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