Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI

• URL: http://arxiv.org/abs/2211.07966v1
• Date: Tue, 15 Nov 2022 08:02:54 GMT
• Title: Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
• Authors: Yeqi Wang, Weijian Huang, Cheng Li, Xiawu Zheng, Yusong Lin, Shanshan Wang
• Abstract summary: 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. However, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patients physiological limitations. It is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic.
• Score: 11.231836756951655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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