| Abstrakt: |
Many strategies of segmentation of medical images are based on supervised voxel classification. These approaches usually work best if a training kit is given that describes the test images by line. However, complications may occur in training and analyzing results, such as due to variations in scanners, procedures, or patient classes, which follow different distributions. Even under the circumstances, weighing pictures based on delivery similarities has demonstrated a substantial improvement in inefficiency. This suggests that part of the training examples represents test information; it makes non-representative data more comparable. Thus, we analyze kernel education to minimize distinctions between training and test data and investigate an additional benefit for picture weighting in kernel learning. Furthermore, we suggest a new image measurement process, minimizing the maximum mean difference between training and test results, improving image weight, and kernel joint optimization. Brain tissue tests, a brain structure lesion, and personalization of the skin suggest that heterogeneous data efficiency significantly boosts both kernel learning and picture weighting when used separately. In this case, MMD weighting works in a manner close to the imaging approaches previously indicated. Integrating picture measurement and matrix modeling will lead to minor performance enhancements, either independently or jointly optimized. [ABSTRACT FROM AUTHOR] |
