"SA-LuT-Nets: Learning Sample-adaptive Intensity Lookup Tables for Brai" by Biting Yu, Luping Zhou et al.
Abstract In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity l...
Source: uow.edu.au