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In the present work we describe a novel training scheme for automated radiograph annotation, as used in post-surgical assessment of Total Knee Replacement. As we show experimentally, standard off-the-shelf methods fail to provide high accuracy image annotations for Total Knee Replacement annotation. We instead adopt a U-Net based segmentation style annotator, relax the task by dilating annotations into larger label regions, then progressively erode these label regions back to the base task on a schedule based on training epoch. We demonstrate the advantages of this scheme on a dataset of radiographs with gold-standard expert annotations, comparing against four baseline cases.
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