Weighting training images by maximizing distribution similarity for supervised segmentation across scanners
We present a transfer-learning approach to image segmentation. Training images are given a weight which is used in a weighted classifier. Weights maximize feature distribution similarity between training and target data. In experiments on brain MRI, weighting improved segmentation results up to 42%. This method is useful for heterogeneous image data, e.g. in multi-center studies.
brain mri domain adaptation machine learning transfer learning