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Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children.
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects.
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