Spatial-channel relation learning for brain tumor segmentation.
Journal:
Medical physics
Published Date:
Aug 26, 2020
Abstract
PURPOSE: Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in magnetic resonance imaging data and linear fusion omitting semantic gaps between features in different branches remain challenging. We need to design a mechanism to fully utilize the similarity within the spatial space and channel space and the correlation between these two spaces to improve the result of volumetric segmentation.