Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
This addresses a practical issue for clinicians by enabling diagnosis when contrast-enhanced MRI is infeasible due to patient limitations or cost, though it is incremental as it builds on existing multi-contrast MRI methods.
The paper tackles the problem of auxiliary glioma diagnosis without contrast-enhanced MRI by proposing an adaptive PromptNet that uses only non-enhanced MRI data, achieving competitive glioma grading performance on the BraTS2020 dataset.
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.