Robust classification techniques for medical imaging

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Machine learning techniques have had an enormous impact on the field of medical image analysis and have been successfully applied to a wide variety of classification, segmentation and registration problems. There are, however, significant challenges in taking a classification algorithm trained on one specific research dataset and translating it to routine clinical practice where images acquired at different centres in different patient populations may have very different characteristics. The problem is made even more difficult by the high cost of collecting good quality training images complete with expert annotations or ground truth diagnoses.