  Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary
glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI
sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most
of the existing relevant studies, in which remarkable diagnosis results have
been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes
not feasible due to the patients physiological limitations. Furthermore, it is
more time-consuming and costly to collect contrast-enhanced MRI data in the
clinic. In this paper, we propose an adaptive PromptNet to address these
issues. Specifically, a PromptNet for glioma grading utilizing only
non-enhanced MRI data has been constructed. PromptNet receives constraints from
features of contrast-enhanced MR data during training through a designed prompt
loss. To further boost the performance, an adaptive strategy is designed to
dynamically weight the prompt loss in a sample-based manner. As a result,
PromptNet is capable of dealing with more difficult samples. The effectiveness
of our method is evaluated on a widely-used BraTS2020 dataset, and competitive
glioma grading performance on NE-MRI data is achieved.
